The most common somatic alteration in primary prostate cancer is the TMPRSS2:ERG gene fusion, which may be caused or promoted by distinct etiologic factors. The objective of this systematic review was to assess epidemiologic evidence on etiologic factors for prostate cancer by tumor TMPRSS2:ERG fusion status in human populations. Of 3071 publications identified, 19 cohort or case-control studies from six distinct study populations were included in this systematic review. Etiologic factors included germline genetic variants, circulating hormones, and dietary and lifestyle factors. Taller height, higher total and free testosterone levels, and fewer trinucleotide repeats in AR were possibly associated with higher risk of TMPRSS2:ERG-positive prostate cancer. Excess body weight, greater vigorous physical activity, higher lycopene intake, and the use of calcium channel blockers were associated with lower risk of TMPRSS2:ERG-positive prostate cancer. Diabetes and family history of prostate cancer were associated with both TMPRSS2:ERG-positive and TMPRSS2:ERG-negative prostate cancer. Prostate cancer germline variants had suggestive differential associations with TMPRSS2:ERG-positive or TMPRSS2:ERG-negative prostate cancer. However, results were based on few distinct study populations and generally had low precision, underscoring the need for replication. In conclusion, prostate cancer with TMPRSS2:ERG fusion is an etiologically distinct subtype that may be, in part, preventable by addressing modifiable and hormonally acting etiologic factors that align with the established mechanistic role of TMPRSS2:ERG in androgen, insulin, antioxidant, and growth factor pathways.

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Oncogenic gene fusions occur across a broad range of cancers and are a defining feature of some cancer types. Cancers driven by gene fusion products tend to respond well to targeted therapies, where available; thus, detection of potentially targetable oncogenic fusions is necessary to select optimal treatment. Detection methods include non-sequencing methods, such as fluorescence in situ hybridization and immunohistochemistry, and sequencing methods, such as DNA- and RNA-based next-generation sequencing (NGS). While NGS is an efficient way to analyze multiple genes of interest at once, economic and technical factors may preclude its use in routine care globally, despite several guideline recommendations. The aim of this review is to present a summary of oncogenic gene fusions, with a focus on fusions that affect tyrosine kinase signaling, and to highlight the importance of testing for oncogenic fusions. We present an overview of the identification of oncogenic gene fusions and therapies approved for the treatment of cancers harboring gene fusions, and summarize data regarding treating fusion-positive cancers with no current targeted therapies and clinical studies of fusion-positive cancers. Although treatment options may be limited for patients with rare alterations, healthcare professionals should identify patients most likely to benefit from oncogenic gene fusion testing and initiate the appropriate targeted therapy to achieve optimal treatment outcomes.

The TMPRSS2 cell surface protease is used by a broad range of respiratory viruses to facilitate entry into target cells. Together with ACE2, TMPRSS2 represents a key factor for SARS-CoV-2 infection, as TMPRSS2 mediates cleavage of viral spike protein, enabling direct fusion of the viral envelope with the host cell membrane. Since the start of the COVID-19 pandemic, TMPRSS2 has gained attention as a therapeutic target for protease inhibitors which would inhibit SARS-CoV-2 infection, but little is known about TMPRSS2 regulation, particularly in cell types physiologically relevant for SARS-CoV-2 infection. Here, we performed an unbiased genome-wide CRISPR-Cas9 library screen, together with a library targeted at epigenetic modifiers and transcriptional regulators, to identify cellular factors that modulate cell surface expression of TMPRSS2 in human colon epithelial cells. We find that endogenous TMPRSS2 is regulated by the Elongin BC-VHL complex and HIF transcription factors. Depletion of Elongin B or treatment of cells with PHD inhibitors resulted in downregulation of TMPRSS2 and inhibition of SARS-CoV-2 infection. We show that TMPRSS2 is still utilised by SARS-CoV-2 Omicron variants for entry into colonic epithelial cells. Our study enhances our understanding of the regulation of endogenous surface TMPRSS2 in cells physiologically relevant to SARS-CoV-2 infection.

Extrachromosomal DNA (ecDNA) amplifications are prevalent drivers of human cancers. We show that ecDNAs exhibit elevated structural variants leading to gene fusions that produce oncogene fusion transcripts. The long noncoding RNA (lncRNA) gene PVT1 is the most recurrent structural variant across cancer genomes, with PVT1-MYC fusions arising most frequently on ecDNA. PVT1 exon 1 is the predominant 5' partner fused to MYC or other oncogenes on the 3' end. Mechanistic studies demonstrate that PVT1 exon 1 confers enhanced RNA stability for fusion transcripts, which requires PVT1 exon 1 interaction with SRSF1 protein. Genetic rescue of MYC-addicted cancer models and isoform-specific single-cell RNA sequencing of tumors reveal that PVT1-MYC better supports MYC dependency and better activates MYC target genes in vivo . Thus, the mutagenic landscape of ecDNA contributes to genome instability and generates chimeric fusions of lncRNA and mRNA genes, selecting PVT1 5' region as a stabilizer of oncogene mRNAs.

Next-generation sequencing has revolutionized genome science over the last two decades. Indeed, the wealth of sequence information on our genome has deepened our understanding on cancer. Cancer is a genetic disease caused by genetic or epigenetic alternations that affect the expression of genes that control cell functions, particularly cell growth and division. Utilization of next-generation sequencing in cancer gene panels has enabled the identification of actionable gene alterations in cancer patients to guide personalized precision medicine.

The aim is to provide information that can identify actionable gene alterations, enabling personalized precision medicine for cancer patients.

Equipped with next-generation sequencing techniques, international collaboration programs on cancer genomics have identified numerous mutations, gene fusions, microsatellite variations, copy number variations, and epigenetics changes that promote the transformation of normal cells into tumors. Cancer classification has traditionally been based on cell type or tissue-of-origin and the morphological characteristics of the cancer. However, interactive genomic analyses have currently reclassified cancers based on systemic molecular-based taxonomy. Although all cancer-causing genes and mechanisms have yet to be completely understood or identified, personalized or precision medicine is now currently possible for some forms of cancer. Unlike the "one-size-fits-all" approach of traditional medicine, precision medicine allows for customized or personalized treatment based on genomic information.

Despite the availability of numerous cancer gene panels, technological innovation in genomics and expansion of knowledge on the cancer genome will allow precision oncology to manage even more types of cancers.

Neuroendocrine prostate cancer (NEPC), an aggressive subtype induced by hormone therapy, lacks effective treatments. This study explored the role of E26 transformation-specific variant 5 (ETV5) in NEPC development. Analysis of multiple prostate cancer datasets revealed that NEPC is characterized by significantly elevated ETV5 expression compared to other subtypes. ETV5 expression increased progressively under hormone therapy through epigenetic modifications. ETV5 induced neural stem-like features in prostate cancer cells and facilitated their differentiation into NEPC under hormone treatment conditions, both in vitro and in vivo. Our molecular mechanistic study identified PBX3 and TLL1 as target genes of ETV5 that contribute to ETV5 overexpression-induced castration resistance and stemness. Notably, obeticholic acid, identified as an ETV5 inhibitor in this study, exhibited promising efficacy in suppressing NEPC development. This study highlights ETV5 as a key transcription factor that facilitates NEPC development and underscores its potential as a therapeutic target for this aggressive cancer subtype.

RNA sequencing became a key tool in identifying the differences between cells and their functions, aiding in the recognition of the functional elements disrupted during the disease process. In urologic malignancies, many studies aiming to provide comprehensive molecular classifications through the assessment of RNA expression or fusion analysis have been published. The distinctive presence of these molecular alterations related to cancer development, growth, and survival and the discoveries of these breakthrough studies provide insight into the development of personalized management and aid in the identification of new therapeutic targets.

Prostate cancer (PCa) screening primarily relies on Prostate-Specific Antigen (PSA), which has low specificity and therefore leads to unnecessary biopsies. Consequently, there is a growing need for, ideally, non-invasive biomarkers. Liquid biopsy, a diagnostic approach analyzing circulating tumor components in body fluids, has emerged as a promising diagnostic tool for various cancers, including PCa.

To evaluate recent evidence on urine-based biomarkers for the detection of PCa, we conducted a systematic review in accordance with the PRISMA guidelines. Our literature search identified a total of 286 studies, of which 66 met our inclusion criteria (men suspected of PCa with no prior history of PCa). After assessing the risk of bias using the QUADAS-2 tool, studies on five distinct urinary biomarker tests were included for further analysis.

Tests that do not rely on digital rectal examination (non-DRE), such as Exosome Dx Prostate IntelliScore (EPI) and Protexam Prostate Status Management (PSM)/Prostate Check-Up (PSU), demonstrated strong performance in detecting PCa, particularly clinically significant PCa. Meanwhile, the MyProstateScore test (MPS) showed the highest efficacy among tests utilizing urine samples collected post-DRE. Unfortunately, the performance of the biomarker test with the most available studies, PCA3 ProGensa® Score, was underwhelming with only moderate sensitivity and specificity.

Despite promising results from various urine-based biomarker tests, we are currently unable to recommend one specific test for implementation into clinical practice. The broad heterogeneity of the studies conducted hindered the ability to perform a meta-analysis, and prospective randomized trials providing clinical evidence are still lacking.

Prostate cancer is defined by the suppression of genes that suppress tumours and the activation of proto-oncogenes. These are the hallmarks of prostate cancer, and they have been linked to numerous genomic variations, which lead to unfavourable treatment outcomes. Prostate cancer can be categorised into various risk groups of tumour molecular subtypes grounded in the idea of genomic structural variations connected to TMPRSS2:ERG fusion and loss of PTEN. Research suggests that certain genomic alterations may be more prevalent or exhibit different patterns in prostate cancer tumours across populations. Studies have reported a higher frequency of PTEN loss and TMPRSS2:ERG fusion in prostate tumours of Black/African American men, which may contribute to the more aggressive nature of the disease in this population. Thus, therapeutically important information can be obtained from these structural variations, including correlations with poor prognosis and disease severity.

Peer-reviewed articles from 1998 to 2024 were sourced from PubMed and Google Scholar. During the review process, the following search terms were employed: "Tumour suppressor genes OR variations OR alterations OR oncogenes OR diagnostics OR ethnicity OR biomarkers OR prostate cancer genomics OR prostate cancer structural variations OR tumour and molecular subtypes OR therapeutic implications OR immunotherapy OR immunogenetics."

There was a total of 13,012 results for our search query: 5,903 publications from Google Scholar with the patent and citation unchecked filer options, and 7127 articles from PubMed with the abstract, free full text, and full-text options selected. Unpublished works were not involved. Except for four articles published between 1998 and 1999, all other selected articles published in 2000 and later were considered. However, papers with irrelevant information or redundant or duplicate content were not chosen for this review. Thus, 134 met the inclusion criteria and were ultimately retained for this review.

This review extracted 134 relevant articles about genomic structure variations in prostate cancer. Our findings demonstrate the importance of PTEN and TMPRSS2:ERG fusion and tumour molecular subtyping in prostate cancer precision medicine.

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on selecting features that differentiate known diseases or cell states, they often struggle to identify features that both preserve heterogeneity and reveal subtypes. To address this, we utilized deep metric learning-based feature embedding to explore the statistical properties of features crucial for preserving heterogeneity. Our analysis indicated that features with a notable difference in interquartile range (IQR) between classes hold important subtype information. Guided by this insight, we developed a statistical method called PHet (Preserving Heterogeneity), which employs iterative subsampling and differential analysis of IQR combined with Fisher's method to identify a small set of features that preserve heterogeneity and enhance subtype clustering quality. Validation on public single-cell RNA-seq and microarray datasets demonstrated PHet's ability to maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods. Furthermore, an analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells identified two distinct basal cell subtypes differentiating towards a luminal secretory phenotype using PHet-based features, demonstrating promising results in a real-data application. These results highlight PHet's potential to enhance our understanding of disease mechanisms and cell differentiation, contributing significantly to the field of personalized medicine.

To investigate the effects of circ-CBLB and ETS-1 on the proliferation, apoptosis, and inflammatory cytokine expression in the fibroblast-like synoviocytes (FLSs) from patients with rheumatoid arthritis (RA).

Peripheral blood was collected from 15 pairs of healthy controls (HCs) and patients with RA to isolate peripheral blood mononuclear cells (PBMCs). mRNA expression of circ-CBLB and ETS-1 was determined using qRT-PCR. Levels of the inflammatory markers (ESR, CRP, CCP, and RF) were determined, and the 28-joint Disease Activity Score (DAS28) was calculated. For in vitro experiments, human FLS and RA-FLS were cultured, and constructs (pcDNA3.1/siRNA-circ-CBLB, pcDNA3.1/siRNA-ETS-1) were transfected into RA-FLS. Cotransfection of pcDNA3.1-circ-CBLB and siRNA-ETS-1 was undertaken to explore their combined effects. Levels of the key inflammatory cytokines (interleukin [IL]-4, IL-23, IL-13, and tumor necrosis factor [TNF]-α) were evaluated using qRT-PCR and enzyme-linked immunosorbent assays. Functional assays (CCK-8) were used to assess cell viability, apoptosis (flow cytometry), and migration. Western blotting was used to determine protein expression.

In vivo analysis showed significant downregulation of circ-CBLB and ETS-1 in PBMCs from patients with RA compared with the HCs, as confirmed using qRT-PCR. Correlation analysis indicated a positive association among circ-CBLB, ETS-1, and IL-4, while circ-CBLB and ETS-1 were negatively correlated with inflammatory markers (ESR, CRP, RF, CCP, DAS28, IL-23, and TNF-α). Receiver operating characteristic curve analysis suggested circ-CBLB and ETS-1 as potential biomarkers for high disease activity in RA. In vitro, circ-CBLB overexpression increased IL-4 levels while decreasing IL-23, IL-13, and TNF-α levels. Additionally, circ-CBLB inhibited the apoptosis of RA-FLS, prolonged the cell cycle, and reduced cell migration. ETS-1 negatively regulated circ-CBLB, indicating a feedback loop.

circ-CBLB and ETS-1 are downregulated in RA and correlate with inflammation and disease activity. They regulate each other bidirectionally. circ-CBLB reduces RA-FLS viability, promotes apoptosis, and inhibits migration by modulating cytokines. ETS-1 has similar effects, and interfering with its expression reverses the impact of circ-CBLB.

TMPRSS2::ETS fusions occur in about half of the prostate adenocarcinomas and are generally considered characteristic and even diagnostic of this tumour. Despite this fact, the frequency of this fusion is not well established in other tumour types. Incidental identification of a TMPRSS2::ETV1 fusion in a thymic squamous cell carcinoma prompted us to assess 1758 solid tumours by next-generation sequencing panel for TMPRSS2::ETS fusions, including 1690 non-prostatic malignancies. In addition to 28 of the 68 prostatic adenocarcinomas, we identified TMPRRS2::ETS fusion in two non-prostatic tumours, where a prostatic adenocarcinoma could be confidently excluded. Besides the TMPRSS2::ETV1 fusion identified in the thymic carcinoma, we also observed TMPRSS2::ERG fusion in pulmonary adenocarcinoma. Both patients were male, which would support an androgen-driven background. Our findings suggest that although at a low frequency, TMPRSS2::ETS fusion may occur in non-prostatic tumours, and sole reliance on its identification can potentially result in erroneous diagnostic conclusions.

Despite incredible progress in describing the molecular underpinnings of prostate cancer over the last decades, pathologic examination remains indispensable for predicting aggressive behavior in the localized setting. Beyond pathologic grade, specific histologic findings have emerged as critical prognostic or predictive indicators. Here, the authors review molecular correlates of aggressive histologic subtypes of prostate cancer in the localized setting, demonstrating that many of the signature molecular alterations found in metastatic disease-such as tumor suppressor gene loss and DNA repair defects-are enriched in primary disease with adverse histologic features, presaging aggressive behavior, and presenting opportunities for earlier germline screening or targeted therapies.

Despite improving diagnostic possibilities, the incidence of prostate cancer is increasing, but we are not able to reduce the mortality rate. While PSA, 4K score, PCA3 and other urinary markers, ExoDX, SelectMDX, Confirm MDx or MiPS tests are used to identify potential prostate cancer carriers, Decipher, Prolaris or Oncotype DX tests are used to assess the aggressiveness of proven cancer in order to stratify patients for early or delayed treatment. More modern forms of treatment for advanced disease include second-generation antiandrogens and PARP inhibitors. By assessing genetic mutations (e.g. BRCA1, BRCA2 genes, single nucleotide polymorphism) or the presence of splice variants of the androgen receptor (ARV7), we are able to identify patients in whom the planned treatment may be expected to be ineffective and thus choose other treatment modalities. In the present review article, we offer a comprehensive overview of current diagnostic tests that find application in the diagnosis of early and advanced prostate cancer.

Clear differentiation of high-grade and clinically insignificant prostate cancer (PCa) is critical for clinical decision-making. Here, we developed a proprietary urinary exosome isolation approach (EVLatch) and established a facile diagnostic workflow. We discovered that EEF1A1 levels, abundantly expressed on urinary exosomes, positively correlate to urinary exosome counts irrespective of source and collection time and demonstrated that EEF1A1 enables in-assay quantification of urinary exosomes. Importantly, a prostate cancer urinary EVLatch-based artificial intelligence diagnostics (PURE-AID) classification system utilizing PCA3, HOXC6, and DLX1 as targets with SPDEF for reference and EEF1A1 for quality checking, trained on 271 patients, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 in the test set of 351 patients. Combination of PURE-AID with prostate-specific antigen (PSA) and age increases AUROC to 0.80 and reduces 54.3% of unnecessary biopsies with 86.8% sensitivity. Our study provides a new classification system for differentiating high-grade PCa in a workflow- and patient-friendly manner.

Coronavirus disease 2019 (COVID-19) is associated with a wide variety of clinical manifestations.

This study aims to evaluate the levels of angiotensin-converting enzyme 2 (ACE2), metalloprotease 17 (ADAM17), Interleukin-17A (IL-17A), transmembrane serine protease 2 (TMPRSS2), apelin (AP), and vitamin D (VD) biomarkers in nasopharyngeal swab (NPS), serum, and saliva, as well as the change in their values depending on the health status of individuals.

The analysis was performed by using enzyme-linked immunosorbent assay (ELISA) methods.

Comparing the levels of the investigated markers in saliva, we found significantly elevated ACE2 values in vaccinated patients, followed by those with severe COVID-19, compared to healthy, previously infected, and mild COVID-19 groups. For TMPRSS2, IL-17A, ADAM-17, and AP, values were significantly higher in all non-healthy groups (previously infected, mild, and severe COVID-19) compared to healthy individuals. Serum levels of VD were consistently low across all five studied groups, suggesting values below normal ranges. Analysis of marker data in saliva, NPS, and serum revealed a positive correlation between NPS and serum and saliva and serum, as well as between saliva and NPS for all studied markers.

In summary, monitoring changes in biomarkers present in Saliva holds promise as a predictive tool for various diseases. This approach enables the early implementation of preventive measures and protective strategies, potentially improving overall health outcomes.

The ligand-binding pocket of the androgen receptor (AR) is the targeting site of all clinically used AR antagonists. However, various drug-resistant mutations emerged in the pocket. We previously reported a new targeting site at the dimer interface of AR (dimer interface pocket) and identified a novel antagonist M17-B15 that failed in oral administration. In this study, the head part of M17-B15 was substituted with divergent structures. Potent antagonist Z10 with benzo[b]oxepine was first identified. Subsequent structural optimization on the 2-oxopropyl moiety of Z10 generated the more powerful Y5 (IC50 = 0.04 μM). Out of the ordinary, Y5 demonstrated dual mechanisms of action, antagonized AR by disrupting AR dimerization, and induced AR degradation via the ubiquitin-proteasome pathway. Furthermore, Y5 exhibited excellent activity against variant drug-resistant AR mutants comparable to recently approved darolutamide. Furthermore, Y5 effectively suppressed the tumor growth of the LNCaP xenograft via oral administration, providing a potential novel therapeutic for drug-resistant prostate cancer.

The study examines the utility of AMACR, ERG, and AR immunostains in diagnosing prostatic adenocarcinoma (PCa) and assessing prognosis in comparison to the Gleason score and new WHO grading groups. Seventeen PCa biopsies and five benign prostatic hyperplasia (BPH) biopsies were analyzed. Immunoreactivity, scored from 1 to 3 based on percentage of positive cells and intensity of expression, was assessed, revealing 76.47% positivity for AMACR, 35.29% for ERG, and 94.12% for AR in PCa cases, with variable scores and intensity among markers and grade groups. AMACR sensitivity and ERG specificity were noted. Higher-grade PCa exhibited increased positivity for both markers, indicating prognostic significance. In BPH cases, AMACR showed positivity in 2 cases, ERG in 1, and AR in all cases, albeit with lower expression. Differential expression was observed among immunomarkers and grade groups of malignancy. AMACR and ERG stains serve as sensitive and specific markers for PCa diagnosis and prognosis. Their increasing positivity with higher-grade groups underscores prognostic value. These findings highlight the importance of immunostains in refining PCa diagnosis and prognostication.

Lineage plasticity and histologic transformation from prostate adenocarcinoma to neuroendocrine (NE) prostate cancer (NEPC) occur in up to 15% to 20% of patients with castration-resistant prostate cancer (CRPC) as a mechanism of treatment resistance and are associated with aggressive disease and poor prognosis. NEPC tumors typically display small cell carcinoma morphology with loss of androgen receptor (AR) expression and gain of NE lineage markers. However, there is a spectrum of phenotypes that are observed during the lineage plasticity process, and the clinical significance of mixed histologies or those that co-express AR and NE markers or lack all markers is not well defined. Translational research studies investigating NEPC have used variable definitions, making clinical trial design challenging. In this manuscript, we discuss the diagnostic workup of metastatic biopsies to help guide the reproducible classification of phenotypic CRPC subtypes. We recommend classifying CRPC tumors based on histomorphology (adenocarcinoma, small cell carcinoma, poorly differentiated carcinoma, other morphologic variant, or mixed morphology) and IHC markers with a priority for AR, NK3 homeobox 1, insulinoma-associated protein 1, synaptophysin, and cell proliferation based on Ki-67 positivity, with additional markers to be considered based on the clinical context. Ultimately, a unified workup of metastatic CRPC biopsies can improve clinical trial design and eventually practice.

Prostate cancer, the most common malignancy in men, has a relatively favourable prognosis. However, when it spreads to the bone, the survival rate drops dramatically. The development of bone metastases leaves patients with aggressive prostate cancer, the leading cause of death in men. Moreover, bone metastases are incurable and very painful. Hepatocyte growth factor receptor (MET) and fusion of genes encoding E26 transformation-specific (ETS) transcription factors are both involved in the progression of the disease. ETS gene fusions, in particular, have the ability to induce the migratory and invasive properties of prostate cancer cells, whereas MET receptor, through its signalling cascades, is able to activate transcription factor expression. MET signalling and ETS gene fusions are intimately linked to high-grade prostate cancer. However, the collaboration of these factors in prostate cancer progression has not yet been investigated. Here, we show, using cell models of advanced prostate cancer, that ETS translocation variant 1 (ETV1) and transcriptional regulator ERG (ERG) transcription factors (members of the ETS family) promote tumour properties, and that activation of MET signalling enhances these effects. By using a specific MET tyrosine kinase inhibitor in a humanised hepatocyte growth factor (HGF) mouse model, we also establish that MET activity is required for ETV1/ERG-mediated tumour growth. Finally, by performing a comparative transcriptomic analysis, we identify target genes that could play a relevant role in these cellular processes. Thus, our results demonstrate for the first time in prostate cancer models a functional interaction between ETS transcription factors (ETV1 and ERG) and MET signalling that confers more aggressive properties and highlight a molecular signature characteristic of this combined action.

Prostate cancer (PCa) is the second leading cause of cancer-related death in men. The increase in incidence rates of more advanced and aggressive forms of the disease year-to-year fuels urgency to find new therapeutic interventions and bolster already established ones. PCa is a uniquely targetable disease in that it is fueled by male hormones (androgens) that drive tumorigenesis via the androgen receptor or AR. Current standard-of-care therapies directly target AR and its aberrant signaling axis but resistance to these therapies commonly arises, and the mechanisms behind the onset of therapy-resistance are still elusive. Research has shown that even with resistant disease, AR remains the main driver of growth and survival of PCa, and AR target genes and cofactors may help mediate resistance to therapy. Here, we focused on a homeobox transcription factor that exhibits a close relationship with AR-NKX3.1. Though NKX3.1 is traditionally thought of as a tumor suppressor, it has been previously reported to promote cancer cell survival by cooperating with AR. The role of NKX3.1 as a tumor suppressor perhaps in early-stage disease also contradicts its profile as a diagnostic biomarker for advanced prostate cancer.

We investigated the physical interaction between NKX3.1 and AR, a modulated NKX3.1 expression in prostate cancer cells via overexpression and knockdown and assayed subsequent viability and downstream target gene expression.

We find that the expression of NKX3.1 is maintained in advanced PCa, and it is often elevated because of aberrant AR activity. Transient knockdown experiments across various PCa cell line models reveal NKX3.1 expression is necessary for survival. Similarly, stable overexpression of NKX3.1 in PCa cell lines reveals an androgen insensitive phenotype, suggesting NKX3.1 is sufficient to promote growth in the absence of an AR ligand.

Our work provides new insight into NKX3.1's oncogenic influence on PCa and the molecular interplay of these transcription factors in models of late-stage prostate cancer.

Background: The limited diagnostic accuracy of prostate-specific antigen screening for prostate cancer (PCa) has prompted innovative solutions, such as the state-of-the-art 18-gene urine test for clinically-significant PCa (MyProstateScore2.0 (MPS2)). Objective: We aim to develop a non-invasive biomarker test, the simplified MPS2 (sMPS2), which achieves similar state-of-the-art accuracy as MPS2 for predicting high-grade PCa but requires substantially fewer genes than the 18-gene MPS2 to improve its accessibility for routine clinical care. Methods: We grounded the development of sMPS2 in the Predictability, Computability, and Stability (PCS) framework for veridical data science. Under this framework, we stress-tested the development of sMPS2 across various data preprocessing and modeling choices and developed a stability-driven PCS ranking procedure for selecting the most predictive and robust genes for use in sMPS2. Results: The final sMPS2 model consisted of 7 genes and achieved a 0.784 AUROC (95% confidence interval, 0.742-0.825) for predicting high-grade PCa on a blinded external validation cohort. This is only 2.3% lower than the 18-gene MPS2, which is similar in magnitude to the 1-2% in uncertainty induced by different data preprocessing choices. Conclusions: The 7-gene sMPS2 provides a unique opportunity to expand the reach and adoption of non-invasive PCa screening.

Prostate cancer is the most common diagnosed tumor and the fifth cancer related death among men in Europe. Although several genetic alterations such as ERG-TMPRSS2 fusion, MYC amplification, PTEN deletion and mutations in p53 and BRCA2 genes play a key role in the pathogenesis of prostate cancer, specific gene alteration signature that could distinguish indolent from aggressive prostate cancer or may aid in patient stratification for prognosis and/or clinical management of patients with prostate cancer is still missing. Therefore, here, by a multi-omics approach we describe a prostate cancer carrying the fusion of TMPRSS2 with ERG gene and deletion of 16q chromosome arm.

We have observed deletion of KDM6A gene, which may represent an additional genomic alteration to be considered for patient stratification. The cancer hallmarks gene signatures highlight intriguing molecular aspects that characterize the biology of this tumor by both a high hypoxia and immune infiltration scores. Moreover, our analysis showed a slight increase in the Tumoral Mutational Burden, as well as an over-expression of the immune checkpoints. The omics profiling integrating hypoxia, ROS and the anti-cancer immune response, optimizes therapeutic strategies and advances personalized care for prostate cancer patients.

The here data reported can lay the foundation for predicting a poor prognosis for the studied prostate cancer, as well as the possibility of targeted therapies based on the modulation of hypoxia, ROS, and the anti-cancer immune response.

Previous evolutionary models of duplicate gene evolution have overlooked the pivotal role of genome architecture. Here, we show that proximity-based regulatory recruitment by distally duplicated genes is an efficient mechanism for modulating tissue-specific production of preexisting proteins. By leveraging genomic asymmetries, we performed a coexpression analysis on Drosophila melanogaster tissue data to show the generality of enhancer capture-divergence (ECD) as a significant evolutionary driver of asymmetric, distally duplicated genes. We use the recently evolved gene HP6/Umbrea as an example of the ECD process. By assaying genome-wide chromosomal conformations in multiple Drosophila species, we show that HP6/Umbrea was inserted near a preexisting, long-distance three-dimensional genomic interaction. We then use this data to identify a newly found enhancer (FLEE1), buried within the coding region of the highly conserved, essential gene MFS18, that likely neofunctionalized HP6/Umbrea. Last, we demonstrate ancestral transcriptional coregulation of HP6/Umbrea's future insertion site, illustrating how enhancer capture provides a highly evolvable, one-step solution to Ohno's dilemma.

Cancer is characterized by uncontrolled cell growth and spreading throughout the body. This study employed computational approaches to investigate 18 naturally derived anticancer piscidinol A derivatives (1-18) as potential therapeutics. By examining their interactions with 15 essential target proteins (HIF-1α, RanGAP, FOXM1, PARP2, HER2, ERα, NGF, FAS, GRP78, PRDX2, SCF complex, EGFR, Bcl-xL, ERG, and HSP70) and comparing them with established drugs such as camptothecin, docetaxel, etoposide, irinotecan, paclitaxel, and teniposide, compound 10 emerged as noteworthy. In molecular dynamics simulations, the protein with the strongest binding to the crucial 1A52 protein exceeded druglikeness criteria and displayed extraordinary stability within the enzyme's pocket over varied temperatures (300-320 K). Additionally, density functional theory was used to calculate dipole moments and molecular orbital characteristics, as well as analyze the thermodynamic stability of the putative anticancer derivatives. This finding reveals a well-defined, potentially therapeutic relationship supported by theoretical analysis, which is in good agreement with subsequent assessments of their potential in vitro cytotoxic effects of piscidinol A derivatives (6-18) against various cancer cell lines. Future in vivo and clinical studies are required to validate these findings further. Compound 10 thus emerges as an intriguing contender in the fight against cancer.

Fusion genes are important biomarkers in cancer research because their expression can produce abnormal proteins with oncogenic properties. Long-read RNA sequencing (long-read RNA-seq), which can sequence full-length mRNA transcripts, facilitates the detection of such fusion genes. Several tools have been proposed for detecting fusion genes in long-read RNA-seq datasets derived from cancer cells. However, the high sequencing error rate in long-read RNA-seq makes fusion gene detection challenging.

To address this issue, additional steps were incorporated into the fusion detection tool to improve detection accuracy. These steps include anchoring breakpoints to exon boundaries, realigning unaligned regions, and clustering breakpoints. To evaluate the accuracy of our tool in detecting fusion genes, we compared its detection accuracy with two representative existing tools, JAFFAL and FusionSeeker.

Our tool outperformed the two existing tools in detecting fusion genes, as demonstrated in long-read RNA-seq datasets. We also identified potentially novel fusion genes consistently detected across multiple tools or datasets.

The application of our tool to the detection of fusion genes in long-read RNA-seq datasets from two different cancer cell lines demonstrated the detection effectiveness of this tool.

Vigorous physical activity has been associated with lower risk of fatal prostate cancer. However, mechanisms contributing to this relationship are not understood.

We studied 117 men with prostate cancer in the University of North Carolina Cancer Survivorship Cohort (UNC CSC) who underwent radical prostatectomy and 101 radiation-treated patients with prostate cancer in FASTMAN. Structured questionnaires administered in UNC CSC assessed physical activity. In both studies, digital image analysis of hematoxylin and eosin-stained tissues was applied to quantify tumor-infiltrating lymphocytes in segmented regions. NanoString gene expression profiling in UNC CSC and microarray in FASTMAN were performed on tumor tissue, and a 50-gene signature utilized to predict immune cell types.

Vigorous recreational activity, reported by 34 (29.1%) UNC CSC men, was inversely associated with tumor-infiltrating lymphocyte abundance. Tumors of men reporting any vigorous activity versus none showed lower gene expression-predicted abundance of Th, exhausted CD4 T cells, and macrophages. T-cell subsets, including regulatory T cells, Th, Tfh, exhausted CD4 T cells, and macrophages, were associated with an increased risk of biochemical recurrence, only among men with ERG-positive tumors.

Vigorous activity was associated with lower prostate tumor inflammation and immune microenvironment differences. Macrophages and T-cell subsets, including those with immunosuppressive roles and those with lower abundance in men reporting vigorous exercise, were associated with worse outcomes in ERG-positive prostate cancer.

Our novel findings contribute to our understanding of the role of the tumor immune microenvironment in prostate cancer progression and may provide insights into how vigorous exercise could affect prostate tumor biology.

TMPRSS2:ERG gene fusion negatively regulates PSMA expression in prostate adenocarcinoma (PCa) cell lines. Therefore, immunohistochemical (IHC) ERG expression, a surrogate for an underlying ERG rearrangement, and PSMA expression patterns in radical prostatectomy (RPE) specimens of primary PCa, including corresponding PSMA-PET scans were investigated.

Two cohorts of RPE samples (total n=148): In cohort #1 (n=62 patients) with available RPE and preoperative [68Ga]Ga-PSMA-11 PET, WHO/ISUP grade groups, IHC-ERG (positive vs. negative) and IHC-PSMA expression (% PSMA-negative tumour area, PSMA%neg) were correlated with the corresponding SUVmax. In the second cohort #2 (n=86 patients) including RPE only, same histopathological parameters were evaluated.

Cohort #1: PCa with IHC-ERG expression (35.5%) showed significantly lower IHC-PSMA expression and lower SUVmax values on the corresponding PET scans. Eight of 9 PCa with negative PSMA-PET scans had IHC-ERG positivity, and confirmed TMPRSS2::ERG rearrangement. In IHC-PSMA positive PCa, IHC-ERG positivity was significantly associated with lower SUVmax values. In cohort #2, findings of higher IHC-PSMA%neg and IHC-ERG expression was confirmed with only 0-10% PSMA%neg tumour areas in IHC-ERG-negative PCa.

IHC-ERG expression is significantly associated with more heterogeneous and lower IHC-PSMA tissue expression in two independent RPE cohorts. There is a strong association of ERG positivity in RPE tissue with lower [68Ga]Ga-PSMA-11 uptake on corresponding PET scans. Results may serve as a base for future biomarker development to enable tumour-tailored, individualized imaging approaches.

Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.

This case emphasizes the challenges in diagnosing and treating Prostatic Duct Adenocarcinoma, especially in resource-limited settings. Early and accurate diagnosis is crucial for better patient outcomes, but limited access to advanced diagnostics and specialized treatments significantly hinders the effective management of this aggressive form of prostate cancer.

Prostate cancer (PCa) is the most prevalent malignancy and the second leading cause of cancer-related deaths among men. While adenocarcinoma of the prostate (adeno-PCa) is well-characterized, neuroendocrine prostate cancer (NEPC) remains poorly understood. Generally, NEPC is a rare but highly aggressive histological variant, however its limited patho-physiological understanding leads to insufficient treatment options associated with low survival rates for NEPC patients. Current treatments for NEPC, including platinum-based therapies, offer some efficacy, but there is a significant need for more targeted approaches. This review summarizes the molecular characteristics of NEPC in contrast to adeno-PCa, providing a comprehensive comparison. A significant portion of the discussion is dedicated to the tumor microenvironment (TME), which has recently been identified as a key factor in tumor progression. The TME includes various cells, signaling molecules, and the extracellular matrix surrounding the tumor, all of which play critical roles in cancer development and response to treatment. Understanding the TME's influence on NEPC could uncover new avenues for innovative treatment strategies, potentially improving outcomes for patients with this challenging variant of PCa.

Biallelic loss of cyclin-dependent kinase 12 (CDK12) defines a metastatic castration-resistant prostate cancer (mCRPC) subtype. It remains unclear, however, whether CDK12 loss drives prostate cancer (PCa) development or uncovers pharmacologic vulnerabilities. Here, we show Cdk12 ablation in murine prostate epithelium is sufficient to induce preneoplastic lesions with lymphocytic infiltration. In allograft-based CRISPR screening, Cdk12 loss associates positively with Trp53 inactivation but negatively with Pten inactivation. Moreover, concurrent Cdk12/Trp53 ablation promotes proliferation of prostate-derived organoids, while Cdk12 knockout in Pten-null mice abrogates prostate tumor growth. In syngeneic systems, Cdk12/Trp53-null allografts exhibit luminal morphology and immune checkpoint blockade sensitivity. Mechanistically, Cdk12 inactivation mediates genomic instability by inducing transcription-replication conflicts. Strikingly, CDK12-mutant organoids and patient-derived xenografts are sensitive to inhibition or degradation of the paralog kinase, CDK13. We therein establish CDK12 as a bona fide tumor suppressor, mechanistically define how CDK12 inactivation causes genomic instability, and advance a therapeutic strategy for CDK12-mutant mCRPC.

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Castration resistant prostate cancer (CRPC) remains an incurable disease stage with ineffective treatments options. Here, the androgen receptor (AR) coactivators CBP/p300, which are histone acetyltransferases, were identified as critical mediators of DNA damage repair (DDR) to potentially enhance therapeutic targeting of CRPC. Key findings demonstrate that CBP/p300 expression increases with disease progression and selects for poor prognosis in metastatic disease. CBP/p300 bromodomain inhibition enhances response to standard of care therapeutics. Functional studies, CBP/p300 cistrome mapping, and transcriptome in CRPC revealed that CBP/p300 regulates DDR. Further mechanistic investigation showed that CBP/p300 attenuation via therapeutic targeting and genomic knockdown decreases homologous recombination (HR) factors in vitro, in vivo, and in human prostate cancer (PCa) tumors ex vivo. Similarly, CBP/p300 expression in human prostate tissue correlates with HR factors. Lastly, targeting CBP/p300 impacts HR-mediate repair and patient outcome. Collectively, these studies identify CBP/p300 as drivers of PCa tumorigenesis and lay the groundwork to optimize therapeutic strategies for advanced PCa via CBP/p300 inhibition, potentially in combination with AR-directed and DDR therapies.