Abstract Background: Androgen receptor pathway inhibitors (ARPIs) improve outcomes for patients with mCRPC. However, development of resistance to ARPIs is a significant clinical issue associated with the emergence of aggressive variant prostate cancer (AVPC), and consequently there is an urgent need for novel, AR pathway-independent therapies. Expression of the metalloprotease methionine aminopeptidase 2 (METAP2) has been correlated with increased mCRPC aggressiveness: high expression was reported in dedifferentiated phenotypes, including NEPC/AVPC. METAP2 regulates protein translation, post-translational modifications and has a clinically validated role inhibiting angiogenesis. METAP2 also has tumor-specific functions coordinating plasticity, vascular mimicry, and hypoxia response. Evexomostat (SDX-7320) is a prodrug of a highly potent, novel METAP2 inhibitor which has completed a phase I safety study in late-stage cancer patients (NCT02743637) and is currently being clinically investigated in patients with metastatic breast cancer (NCT05570253, NCT05455619). It was hypothesized that SDX-7320 would demonstrate anti-tumor efficacy in non-clinical prostate cancer cell-derived xenograft and patient-derived xenograft (PDX) models of ARPI-resistant CRPC and AVPC. Methods: SDX-7320 (12 or 8 mg/kg, subcutaneous dosing, every four days) was tested in NSG mice with LNCaP xenografts in intact, castrated, and CRPC models. SDX-7320 treatment was also evaluated in LuCaP35.CR PDX xenografts in castrate mice alone as well as in combination with enzalutamide following development of resistance to enzalutamide. SDX-7320 was also tested in the LTL545, LUCAP49 and LTL331R (AR-negative, NE-positive) models of AVPC. Tumor growth was assessed and following dissection subsequently analyzed for transcriptomic (RNAseq), protein (Western blot) or histological differences (H&E staining, CD34 IHC). Results: SDX-7320 treatment significantly reduced tumor volume in every model and at every PC stage investigated, alone and in combination with enzalutamide (in enzalutamide-resistant tumors), as well as in the LTL545, LUCAP49 and LTL331R AVPC models. Reduced angiogenesis marker CD34 staining was observed in all tumors from SDX-7320-treated mice. Survival of mice treated with SDX-7320 was significantly enhanced, regardless of model phenotype. Downstream analyses of bulk RNAseq and proteomics showed model-specific changes to plasticity regulators c-Myc and the enhancer of zeste homolog 2 (EZH2) indicating an effect of METAP2 inhibition on prostate cancer cellular differentiation. Conclusions: These results show that inhibition of METAP2 with SDX-7320 is a novel approach to treat ARPI-resistant as well as aggressive forms of prostate cancer warranting immediate clinical translation. Building upon the non-clinical data in models of AVPC presented here, combined with the body of clinical experience with SDX-7320 in past and ongoing clinical trials, planning is underway to conduct a pilot clinical trial with SDX-7320 in men with AVPC. Citation Format: Peter Cornelius, Devina Laurencia, Jennifer H. Gunter, Anja Rockstroh, Benjamin A. Mayes, Pierre Dufour, Bradley J. Carver, James M. Shanahan, Colleen C. Nelson. MetAP2 inhibition by evexomostat (SDX-7320) decreases EZH2 and c-Myc and significantly prolongs survival in enzalutamide-resistant and neuroendocrine prostate cancer models [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B031.

Abstract Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR−/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

Treatment-emergent neuroendocrine prostate cancer (tNEPC) is a highly aggressive, anaplastic form of prostate cancer with no effective curative treatments. Accordingly, there is an urgent clinical need to find novel therapeutic targets and/or strategies to improve the survival outcomes of men harbouring tNEPC. Recent studies have identified overexpression of the facilitates chromatin transcription (FACT) complex in anaplastic tumours, and in particular those possessing a neuroendocrine phenotype. Recently, a curaxin-based molecule, CBL0137, has been shown to be an effective targeted-agent for inhibiting FACT function. Therefore, this study aims to assess the effect of CBL0137 as a novel targeted treatment for tNEPC, both as a monotherapy and as a co-therapy alongside the standard platinum-based chemotherapy, cisplatin and characterise the molecular consequences of CBL0137 by multi-omic profiling. By taking advantage of several clinically relevant ex-vivo patient-derived xenograft organoid (PDXO) models, anti-tumour activity of CBL0137 was evaluated and has further probed its therapeutic potential as a companion therapy with cisplatin. This project also conducts a comprehensive multi-omic investigation into the molecular mechanisms enacted by CBL0137. CBL0137 exhibits potent inhibitory effects across all the PDXO models tested, with calculated IC50 values in low micromolar ranges confirming its strong anti-tumour activity against tNEPC. Western blot analysis revealed a marked reduction in tumour proliferation and enhanced apoptotic mechanisms following CBL0137 treatment. Notably, combination treatment with cisplatin and CBL0137 resulted in improved therapeutic efficacy compared to either drug alone, indicating a potential beneficial effect that enhances overall treatment response. Mechanistically, CBL0137 upregulated p53-mediated tumour suppression, and induced cell cycle arrest via p21 in NEPC models. In conclusion, our findings confirmed that CBL0137 demonstrates robust anti-tumour activity across diverse NEPC PDXO models, significantly suppressing tumour proliferation and enhancing therapeutic efficacy when combined with cisplatin. The co-treatment consistently outperformed either drug alone, highlighting its potential as a more effective strategy for aggressive NEPC. Ongoing multi-omic profiling will further unravel the molecular consequences of CBL0137 treatment and support the development of improved therapeutic approaches for patients with anaplastic and treatment-resistant prostate cancer phenotypes. Sayuri Herath, Charles Bidgood, Momin Rahman, Thomas Tang, Nataly Stylianou, Melanie Lehman, Anja Rockstroh, Himisha Beltran, Arun Azad, Katerina Gurova, Yuzhuo Wang, Martin Gleave, Eva Corey, Jennifer Gunter, Colleen Nelson, Brett Hollier. CBL0137 as a promising therapeutic strategy for anaplastic, treatment-emergent neuroendocrine prostate cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C135.

The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layout algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (t-SNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual malignant cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For malignant cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific malignant cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR−/NElow(androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

Metabolic reprogramming is a hallmark of cancer and fundamental for disease progression. The remodelling of oxidative phosphorylation and enhanced lipogenesis are key characteristics of prostate cancer (PCa). Recently, succinate-dependent mitochondrial reprogramming was identified in high-grade prostate tumours with upregulation of enzymes associated with branched-chain amino acid (BCAA) catabolism. We hypothesised that the degradation of BCAAs, particularly valine may play a critical role in anapleurotic refuelling of the mitochondrial succinate pool. Through suppression of valine availability, we report strongly reduced lipid content despite compensatory upregulation of fatty acid uptake, indicating valine is an important lipogenic fuel in PCa. Inhibition of the enzyme 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) also resulted in selective inhibition of cellular proliferation of malignant but not benign prostate cells and impaired succinate production. In combination with a comprehensive multi-omic investigation of patient and cell line data, our work highlights a therapeutic target for selective inhibition of metabolic reprogramming in PCa.### Competing Interest StatementThe authors have declared no competing interest.

Metabolic reprogramming and energetic rewiring are hallmarks of cancer that fuel disease progression and facilitate therapy evasion. The remodelling of oxidative phosphorylation and enhanced lipogenesis have previously been characterised as key metabolic features of prostate cancer (PCa). Recently, succinate-dependent mitochondrial reprogramming was identified in high-grade prostate tumours, as well as upregulation of the enzymes associated with branched-chain amino acid (BCAA) catabolism. In this study, we hypothesised that the degradation of the BCAAs, particularly valine, may play a critical role in anapleurotic refuelling of the mitochondrial succinate pool, as well as the maintenance of intracellular lipid metabolism. Through the suppression of BCAA availability, we report significantly reduced lipid content, strongly indicating that BCAAs are important lipogenic fuels in PCa. This work also uncovered a novel compensatory mechanism, whereby fatty acid uptake is increased in response to extracellular valine deprivation. Inhibition of valine degradation via suppression of 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) resulted in a selective reduction of malignant prostate cell proliferation, decreased intracellular succinate and impaired cellular respiration. In combination with a comprehensive multi-omic investigation that incorporates next-generation sequencing, metabolomics, and high-content quantitative single-cell imaging, our work highlights a novel therapeutic target for selective inhibition of metabolic reprogramming in PCa.

Supplementary Figures S1-S4

Patient-derived xenograft (PDX) models have been established as important preclinical cancer models, overcoming some of the limitations associated with the use of cancer cell lines. The utility of prostate cancer PDX models has been limited by an inability to genetically manipulate them in vivo and difficulties sustaining PDX-derived cancer cells in culture. Viable, short-term propagation of PDX models would allow in vitro transfection with traceable reporters or manipulation of gene expression relevant to different studies within the prostate cancer field. Here, we report an organoid culture system that supports the growth of prostate cancer PDX cells in vitro and permits genetic manipulation, substantially increasing the scope to use PDXs to study the pathobiology of prostate cancer and define potential therapeutic targets. We have established a short-term PDX-derived in vitro cell culture system which enables genetic manipulation of prostate cancer PDXs LuCaP35 and BM18. Genetically manipulated cells could be re-established as viable xenografts when re-implanted subcutaneously in immunocompromised mice and were able to be serially passaged. Tumor growth of the androgen-dependent LuCaP35 PDX was significantly inhibited following depletion of the androgen receptor (AR) in vivo. Taken together, this system provides a method to generate novel preclinical models to assess the impact of controlled genetic perturbations and allows for targeting specific genes of interest in the complex biological setting of solid tumors.

Abstract Background: Prostate cancer (PCa) is the second-most diagnosed malignancy among men and is a leading cause of cancer death worldwide (WHO, 2020). Elevated expression of the enzyme methionine aminopeptidase type 2 (MetAP2) in prostate cancer was recently shown to be associated with higher grade tumors and with worse clinical outcomes (Xie, 2021). Evexomostat/SDX-7320 is a polymer-drug conjugate of a novel fumagillin-derived MetAP2 inhibitor attached to a polymer backbone via a cleavable linker. This design alters biodistribution (limits CNS penetration) and improves pharmacokinetics relative to small molecule fumagillin-derived MetAP2 inhibitors. Evexomostat completed a phase I safety study in late-stage cancer patients and is in two phase 2 breast cancer studies (NCT05455619, NCT05570253). Methods: To evaluate the efficacy of evexomostat in a model of prostate cancer, NSG mice were injected subcutaneously with 2×106 LNCaP cells. When tumors reached ≈200 mm3, mice were randomized into 4 groups. A cohort of intact mice (Group 1; n=4/group) received treatment with SDX-7320 (12 mg/kg, s.c. Q4D) or vehicle (5% mannitol/water). Group 2 were castrated and randomized to SDX-7320 (6 mg/kg, 12 mg/kg) or Vehicle (5% mannitol) upon recovery (n=4/group). Group 3 (n=13/group) and Group 4 (n=14/group) were castrated and randomized to Vehicle or SDX-7320 at recurrence of tumor growth with Group 4 additionally receiving enzalutamide (10 mg/kg, p.o., QD). Tumor growth and body weight were assessed twice/week. Upon necropsy, tumors were dissected, weighed and snap frozen for RNAseq analysis or formalin fixed along with major organs for histological processing (H&E staining, and tumor CD31, CD34 IHC analysis). Results: Evexomostat elicited a significant reduction in tumor growth in the absence of significant changes in body weight. Survival in evexomostat-treated mice was doubled relative to vehicle-treated mice (12.5 vs 23.5 days). In castrated mice (Group 2), evexomostat (6 and 12 mg/kg) attenuated tumor growth with no change in body weight, and in CRPC mice (Group 3), evexomostat also significantly inhibited tumor growth. Treatment of CRPC mice (Group 4) with enzalutamide alone elicited a mixed response, with 6/14 mice showing sustained response to enzalutamide (responders). At endpoint, tumors from enzalutamide non-responders averaged 0.54g (± 0.09g) and enzalutamide responders 0.27g (± 0.07g), p<0.05, while tumors from SDX-7320 plus enzalutamide-treated mice weighed 0.19g (± 0.07g), p<0.005 compared to enzalutamide non-responders. Conclusions: MetAP2 inhibition has potential as a new approach for the treatment of prostate cancer. The clinical-stage MetAP2 inhibitor evexomostat/SDX-7320 significantly inhibited the growth of LNCaP tumors in intact, castrated and CRPC mice. Clinical evaluation of evexomostat in castration-sensitive as well as castration-resistant prostate cancer patients is warranted. Citation Format: Peter Cornelius, Benjamin Mayes, Bradley J. Carver, James Shanahan, Jennifer Gunter, Colleen Nelson. Evexomostat: A novel therapeutic in development for the treatment of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1656.

EB1-GFP was imaged by spinning disk microscopy (2 images/s for 2 min). EB1 comets appeared faint and followed irregular and short trajectories. Scale bar=10 µm.

Genome-wide association studies have linked Iroquois-Homeobox 4 (IRX4) as a robust expression quantitative-trait locus associated with prostate cancer (PCa) risk. However, the intricate mechanism and regulatory factors governing IRX4 expression in PCa remain poorly understood. Here, we unveil enrichment of androgen-responsive gene signatures in metastatic prostate tumors exhibiting heightened IRX4 expression. Furthermore, we uncover a novel interaction between IRX4 and the androgen receptor (AR) co-factor, FOXA1, suggesting that IRX4 modulates PCa cell behavior through AR cistrome alteration. Remarkably, we identified a distinctive short insertion-deletion polymorphism (INDEL), upstream of the IRX4 gene that differentially regulates IRX4 expression through the disruption of AR binding. This INDEL emerges as the most significant PCa risk-associated variant within the 5p15 locus, in a genetic analysis involving 82,591 PCa cases and 61,213 controls and was associated with PCa survival in patients undergoing androgen-deprivation therapy. These studies suggest the potential of this INDEL as a prognostic biomarker for androgen therapy in PCa and IRX4 as a potential therapeutic target in combination with current clinical management.

Supplementary Materials and Methods with detailed methods and primer and antibody lists.

BACKGROUND:Activation and regulation of androgen receptor (AR) signaling and the DNA damage response impact the prostate cancer (PCa) treatment modalities of androgen deprivation therapy (ADT) and radiotherapy. Here, we have evaluated a role for human single-strand binding protein 1 (hSSB1/NABP2) in modulation of the cellular response to androgens and ionizing radiation (IR). hSSB1 has defined roles in transcription and maintenance of genome stability, yet little is known about this protein in PCa.METHODS:We correlated hSSB1 with measures of genomic instability across available PCa cases from The Cancer Genome Atlas (TCGA). Microarray and subsequent pathway and transcription factor enrichment analysis were performed on LNCaP and DU145 prostate cancer cells.RESULTS:Our data demonstrate that hSSB1 expression in PCa correlates with measures of genomic instability including multigene signatures and genomic scars that are reflective of defects in the repair of DNA double-strand breaks via homologous recombination. In response to IR-induced DNA damage, we demonstrate that hSSB1 regulates cellular pathways that control cell cycle progression and the associated checkpoints. In keeping with a role for hSSB1 in transcription, our analysis revealed that hSSB1 negatively modulates p53 and RNA polymerase II transcription in PCa. Of relevance to PCa pathology, our findings highlight a transcriptional role for hSSB1 in regulating the androgen response. We identified that AR function is predicted to be impacted by hSSB1 depletion, whereby this protein is required to modulate AR gene activity in PCa.CONCLUSIONS:Our findings point to a key role for hSSB1 in mediating the cellular response to androgen and DNA damage via modulation of transcription. Exploiting hSSB1 in PCa might yield benefits as a strategy to ensure a durable response to ADT and/or radiotherapy and improved patient outcomes.

EB1-GFP was imaged by spinning disk microscopy (1 image/s for 1 min). EB1 comets followed irregular and short trajectories. Scale bar=10 µm.

Supplementary Figure Legends and Data Table containing developmental reprogramming 59-Gene commonly regulated gene list.