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Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR.Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients’ plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients.Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p < 0.0001) tRF-Lys-CTT-5c (p < 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p < 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers—GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76.Conclusion: This study identified two novel EV-enclosed RNA biomarkers–NKX3-1 and GLO1–for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.

Abstract Hypoxia induces changes in the secretion of extracellular vesicles (EVs) in several non‐neuronal cells and pathological conditions. EVs are packed with biomolecules, such as microRNA(miR)‐21‐5p, which respond to hypoxia. However, the true EV association of miR‐21‐5p, and its functional or biomarker relevance, are inadequately characterised. Neurons are extremely sensitive cells, and it is not known whether the secretion of neuronal EVs and miR‐21‐5p are altered upon hypoxia. Here, we characterised the temporal EV secretion profile and cell viability of neurons under hypoxia. Hypoxia induced a rapid increase of miR‐21a‐5p secretion in the EVs, which preceded the elevation of hypoxia‐induced tissue or cellular miR‐21a‐5p. Prolonged hypoxia induced cell death and the release of morphologically distinct EVs. The EVs protected miR‐21a‐5p from enzymatic degradation but a remarkable fraction of miR‐21a‐5p remained fragile and non‐EV associated. The increase in miR‐21a‐5p secretion may have biomarker potential, as high blood levels of miR‐21‐5p in stroke patients were associated with significant disability at hospital discharge. Our data provides an understanding of the dynamic regulation of EV secretion from neurons under hypoxia and provides a candidate for the prediction of recovery from ischemic stroke.

Abstract Background The FASTA file format, used to store polymeric sequence data, has become a bioinformatics file standard used for decades. The relatively large files require additional files, beyond the scope of the original format, to identify sequences and to provide random access. Multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration. Results We designed a linux based toolkit that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem by using filesystem in userspace. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using bit encodings plus Zstandard (zstd). The toolkit, FASTAFS, can track all its system-wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. The file compression ratios were comparable but not superior to other state of the art archival tools, despite the innovative random access feature implemented in FASTAFS. Conclusions FASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers format conversion without the need to rewrite code into different programming languages while preserving compatibility.

Treatment of prostate cancer (PCa) has changed considerably in the last decade due to the introduction of novel androgen receptor (AR)-targeted agents (ARTAs) for patients progressing on androgen deprivation therapy (ADT). Preclinical research however still relies heavily on AR-negative cell line models. In order to investigate potential differences in castration-resistant PCa (CRPC) growth, we set out to create a comprehensive panel of ARTA-progressive models from 4 androgen-responsive AR wild-type PCa cell lines and analyzed its androgen response as opposed to its ADT-progressive counterparts. Parallel cultures of VCaP, DuCaP, PC346C, and LAPC4 were established in their respective culture media with steroid-stripped fetal calf serum (FCS) [dextran-coated charcoal-stripped FCS (DCC)] without androgen (ADT) or in DCC plus 1 μM of the ARTAs bicalutamide, OH-flutamide, or RD162 (an enzalutamide/apalutamide analog). Cell growth was monitored and compared to those of parental cell lines. Short-term androgen response was measured using cell proliferation 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. qRT-PCR was performed to assess the mRNA expression of markers for AR signaling, steroidogenesis, glucocorticoid receptor (GR) signaling, epithelial-mesenchymal transition (EMT), and WNT signaling. Out of 35 parallel cultures per cell line, a total of 24, 15, 34, and 16 CRPC sublines emerged for VCaP, DuCaP, PC346C, and LAPC4, respectively. The addition of bicalutamide or OH-flutamide significantly increased CRPC growth compared to ADT or RD162. VCaP, DuCaP, and PC346C CRPC clones retained an AR-responsive phenotype. The expression of AR and subsequent androgen response were completely lost in all LAPC4 CRPC lines. Markers for EMT and WNT signaling were found to be elevated in the resilient PC346C model and CRPC derivatives of VCaP, DuCaP, and LAPC4. Although the resistant phenotype is pluriform between models, it seems consistent within models, regardless of type of ARTA. These data suggest that the progression to and the phenotype of the CRPC state might already be determined early in carcinogenesis.

Tumor organoids have been pushed forward as advanced model systems for in vitro oncology drug testing, with the eventual goal to direct personalized cancer treatments. However, drug testing efforts suffer from a large variation in experimental conditions for organoid culturing and organoid treatment. Moreover, most drug tests are restricted to whole-well viability as the sole read-out, thereby losing important information about key biological aspects that might be impacted due to the use of administered drugs. These bulk read-outs also discard potential inter-organoid heterogeneity in drug responses. To tackle these issues, we developed a systematic approach for processing organoids from prostate cancer (PCa) patient-derived xenografts (PDXs) for viability-based drug testing and identified essential conditions and quality checks for consistent results. In addition, we generated an imaging-based drug testing procedure using high-content fluorescence microscopy in living PCa organoids to detect various modalities of cell death. Individual organoids and cell nuclei in organoids were segmented and quantified using a dye combination of Hoechst 33342, propidium iodide and Caspase 3/7 Green, allowing the identification of cytostatic and cytotoxic treatment effects. Our procedures provide important insights into the mechanistic actions of tested drugs. Moreover, these methods can be adapted for tumor organoids originating from other cancer types to increase organoid-based drug test validity, and ultimately, accelerate clinical implementation.

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

The androgen receptor splice variant (AR‐V) 7 in circulating tumor cells (CTCs) is a predictor for resistance to anti‐AR‐targeted treatment, but not to taxane‐based chemotherapy in metastatic castration‐resistant prostate cancer (mCRPC). In this study, we investigated whether the presence of two constitutively active variants (AR‐V3, AR‐V7) and two other conditionally activated variants (AR‐V1, AR‐V9) vs full‐length androgen receptor (AR‐FL) measured in CTCs from patients with mCRPC were associated with outcome to therapy with the taxane cabazitaxel. Blood was collected at baseline and after two cycles of cabazitaxel from 118 mCRPC patients starting cabazitaxel in a prospective phase II trial. CellSearch‐enriched CTCs were enumerated and in parallel characterized for the presence of the AR‐Vs by reverse transcription quantitative polymerase chain reaction. Correlations with CTC and prostate‐specific antigen response to cabazitaxel as well as associations with overall survival (OS) were investigated. All AR‐Vs were frequently present and co‐expressed at frequencies of 31–48% at baseline and at 19–40% after two cycles of cabazitaxel. No specific directions of change in the measured variants were detected between the start of treatment and after two cycles of cabazitaxel. No associations between the presence of AR‐V3 and AR‐V7 and outcome to cabazitaxel were observed. While a reduction in CTCs to

Castration-resistant prostate cancer (CRPC) remains an incurable and lethal malignancy. The development of new CRPC treatment strategies is strongly impeded by the scarcity of representative, scalable and transferable preclinical models of advanced, androgen receptor (AR)-driven CRPC. Here, we present contemporary patient-derived xenografts (PDXs) and matching PDX-derived organoids (PDXOs) from CRPC patients who had undergone multiple lines of treatment. These models were comprehensively profiled at the morphologic, genomic (n = 8) and transcriptomic levels (n = 81). All are high-grade adenocarcinomas that exhibit copy number alterations and transcriptomic features representative of CRPC patient cohorts. We identified losses of PTEN and RB1, MYC amplifications, as well as genomic alterations in TP53 and in members of clinically actionable pathways such as AR, PI3K and DNA repair pathways. Importantly, the clinically observed continued reliance of CRPC tumors on AR signaling is preserved across the entire set of models, with AR amplification identified in four PDXs. We demonstrate that PDXs and PDXOs faithfully reflect donor tumors and mimic matching patient drug responses. In particular, our models predicted patient responses to subsequent treatments and captured sensitivities to previously received therapies. Collectively, these PDX-PDXO pairs constitute a reliable new resource for in-depth studies of treatment-induced, AR-driven resistance mechanisms. Moreover, PDXOs can be leveraged for large-scale tumor-specific drug response profiling critical for accelerating therapeutic advances in CRPC.

Abstract Proliferation and survival of prostate cancer cells are driven by the androgen receptor (AR) upon binding to androgen steroid hormones. Manipulating the AR signalling axis is the focus for prostate cancer therapy; thus, it is crucial to understand the role of androgens and AR on extracellular vesicle (EV) secretion and cargo. In this study, we report that plasma‐derived circulating vesicles consisting of CD9 and double‐positive for CD9 and Prostate Specific Membrane Antigen (PSMA) are increased in patients with advanced metastatic prostate cancer, whereas double positives for CD9 and CD63 small extracellular vesicles (S‐EVs) are significantly higher in patients with localised prostate cancer. Androgen manipulation by dihydrotestosterone (DHT) and the clinical antagonist enzalutamide (ENZ) altered the heterogeneity and size of CD9 positive S‐EVs in AR expressing prostate cancer cells, while assessment of the total number and protein cargo of total S‐EVs was unaltered across different treatment groups. Furthermore, hormone stimulation caused strong and specific effects on the small RNA cargo of S‐EVs. A total of 543 small RNAs were found to be regulated by androgens including miR‐19‐3p and miR‐361‐5p. Analysis of S‐EVs heterogeneity and small RNA cargo may provide clinical utility for prostate cancer and be informative to understand further the mechanism of resistance to androgen targeted therapy in castration‐resistant prostate cancer.

Extracellular vesicles (EVs) are a family of small membrane vesicles that carry information about cells by which they are secreted. Growing interest in the role of EVs in intercellular communication, but also in using their diagnostic, prognostic and therapeutic potential in (bio) medical applications, demands for accurate assessment of their biochemical and physical properties. In this review, we provide an overview of available technologies for EV analysis by describing their working principles, assessing their utility in EV research and summarising their potential and limitations. To emphasise the innovations in EV analysis, we also highlight the unique possibilities of emerging technologies with high potential for further development.

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as "disease genes". Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.