Mechanism of action of nadofaragene firadenovec-vncg.

Effective bladder-preserving therapeutic options are needed for patients with bacillus Calmette-Guérin unresponsive non-muscle-invasive bladder cancer. Nadofaragene firadenovec-vncg (Adstiladrin®) was approved by the US Food and Drug Administration as the first gene therapy in urology and the first intravesical gene therapy indicated for the treatment of adult patients with high-risk bacillus Calmette-Guérin-unresponsive non-muscle-invasive bladder cancer with carcinoma in situ with or without papillary tumors. The proposed mechanism of action underlying nadofaragene firadenovec efficacy is likely due to the pleiotropic nature of interferon-α and its direct and indirect antitumor activities. Direct activities include cell death and the mediation of an antiangiogenic effect, and indirect activities are those initiated through immunomodulation of the innate and adaptive immune responses. The sustained expression of interferon-α that results from this treatment modality contributes to a durable response. This review provides insight into potential mechanisms of action underlying nadofaragene firadenovec efficacy.

ExoDx prostate test as a predictor of outcomes of high-grade prostate cancer - an interim analysis.

BACKGROUND: Patient outcomes were assessed based on a pre-biopsy ExoDx Prostate (EPI) score at 2.5 years of the 5-year follow-up of ongoing prostate biopsy Decision Impact Trial of the ExoDx Prostate (IntelliScore). METHODS: Prospective, blinded, randomized, multisite clinical utility study was conducted from June 2017 to May 2018 (NCT03235687). Urine samples were collected from 1049 men (≥50 years old) with a PSA 2-10 ng/mL being considered for a prostate biopsy. Patients were randomized to EPI vs. standard of care (SOC). All had an EPI test, but only EPI arm received results during biopsy decision process. Clinical outcomes, time to biopsy and pathology were assessed among low (<15.6) or high (≥15.6) EPI scores. RESULTS: At 2.5 years, 833 patients had follow-up data. In the EPI arm, biopsy rates remained lower for low-risk EPI scores than high-risk EPI scores (44.6% vs 79.0%, p < 0.001), whereas biopsy rates were identical in SOC arm regardless of EPI score (59.6% vs 58.8%, p = 0.99). Also in the EPI arm, the average time from EPI testing to first biopsy was longer for low-risk EPI scores compared to high-risk EPI scores (216 vs. 69 days; p < 0.001). Similarly, the time to first biopsy was longer with EPI low-risk scores in EPI arm compared to EPI low-risk scores in SOC arm (216 vs 80 days; p < 0.001). At 2.5 years, patients with low-risk EPI scores from both arms had less HGPC than high-risk EPI score patients (7.9% vs 26.8%, p < 0.001) and the EPI arm found 21.8% more HGPC than the SOC arm. CONCLUSIONS: This follow-up analysis captures subsequent biopsy outcomes and demonstrates that men receiving EPI low-risk scores (<15.6) significantly defer the time to first biopsy and remain at a very low pathologic risk by 2.5-years after the initial study. The EPI test risk stratification identified low-risk patients that were not found with the SOC.

How I Use It: The Exosome Diagnostics (EPI) prostate cancer biomarker utility in urology and primary care.

Prostate-specific antigen (PSA) screening remains the mainstay for early detection of prostate cancer. Although PSA is a nonspecific prostate cancer biomarker, its specificity for high grade prostate cancer can be enhanced by pre-biopsy liquid biomarkers including the Exosome Dx Prostate IntelliScore (EPI) test. EPI is a stand-alone urine genomic test that measures 3 exosome-derived gene expression signatures without the need for digital rectal examination (DRE) or inclusion of standard of care parameters in the test algorithm. EPI has broad clinical utility as a risk stratification tool for clinically significant high grade prostate cancer in men considering diagnostic prostate biopsy (MRI-targeted and systematic biopsy). During the COVID-19 pandemic, the EPI At-Home Collection Kit was introduced and quickly became an important component of tele-urology. The EPI test has emerged as a prioritization tool for primary care referral to urologists and for prostate biopsy scheduling. EPI provides an objective and actionable genomic risk assessment tool for high grade prostate cancer and is a critical part of the informed decision-making regarding biopsy (targeted, systematic or both) in both urology and primary care practices.

Pre-diagnosis urine exosomal RNA (ExoDx EPI score) is associated with post-prostatectomy pathology outcome.

PURPOSE: ExoDx Prostate IntelliScore (EPI) is a non-invasive urine exosome RNA-based test for risk assessment of high-grade prostate cancer. We evaluated the association of pre-biopsy test results with post-radical prostatectomy (RP) outcomes to understand the potential utility of EPI to inform invasive treatment vs active surveillance (AS) decisions. METHODS: Urine samples were collected from 2066 men scheduled for initial biopsy with PSA between 2 and 10 ng/mL, no history of prostate cancer, and ≥ 50 years across multiple clinical studies. 310 men proceeded to RP, of which 111 patients had Gleason group grade 1 (GG1) at biopsy and would have been potential candidates for AS. We compared pre-biopsy urine scores with ERSPC and PCPT multivariate risk calculator scores for men with GG1 at biopsy to post-RP pathology. RESULTS: Urine EPI scores were significantly lower in men with GG1 at biopsy than in men with > GG1 (p = 0.04), while there were no differences in multivariate risk scores used in standard clinical practice (p > 0.05). Further, EPI scores were significantly lower in men with GG1 at biopsy who remained GG1 post-RP compared to men upgraded to ≥ GG3 post-RP (p < 0.001). In contrast, none of the multiparametric risk calculators showed significant differences (p > 0.05). Men with GG1 at biopsy and EPI score < 15.6 had zero rate of upgrading to ≥ GG3 post-RP compared to 16.0% for EPI scores ≥ 15.6. CONCLUSIONS: The EPI urine biomarker outperformed the multivariate risk calculators in a homogenous risk group of pre-biopsy men. The EPI score was associated with low-risk pathology post-RP, with potential implications on informing AS decisions. TRIAL REGISTRATION: NCT02702856, NCT03031418, NCT03235687, NCT04720599.

SuperGAG biopolymers for treatment of excessive bladder permeability.

Few therapeutic options exist for treatment of IC/BPS. A novel high MW GAG biopolymer ("SuperGAG") was synthesized by controlled oligomerization of CS, purified by TFF and characterized by SEC-MALLS and 1H-NMR spectroscopy. The modified GAG biopolymer was tested in an OVX female rat model in which bladder permeability was induced by a 10-minute intravesicular treatment with dilute (1 mg/ml) protamine sulfate and measured by classical Ussing Chamber TEER measurements following treatment with SuperGAG, chondroitin sulfate, or saline. The effect on abrogating the abdominal pain response was assessed using von Frey filaments. The SuperGAG biopolymer was then investigated in a second, genetically modified mouse model (URO-MCP1) that increasingly is accepted as a model for IC/BPS. Permeability was induced with a brief exposure to a sub-noxious dose of LPS and was quantified using contrast-enhanced MRI (CE-MRI). The SuperGAG biopolymer restored impermeability to normal levels in the OVX rat model as measured by TEER in the Ussing chamber and reduced the abdominal pain response arising from induced permeability. Evaluation in the URO-MCP1 mouse model also showed restoration of bladder impermeability and showed the utility of CE-MRI imaging for evaluating the efficacy of agents to restore bladder impermeability. We conclude novel high MW SuperGAG biopolymers are effective in restoring urothelial impermeability and reducing pain produced by loss of the GAG layer on the urothelium. SuperGAG biopolymers could offer a novel and effective new therapy for IC/BPS, particularly if combined with MRI to assess the efficacy of the therapy.

A urine-based Exosomal gene expression test stratifies risk of high-grade prostate Cancer in men with prior negative prostate biopsy undergoing repeat biopsy.

BACKGROUND: Initial prostate biopsy often fails to identify prostate cancer resulting in patient anxiety, especially when clinical features such as prostate specific antigen (PSA) remain elevated, leading to the need for repeat biopsies. Prostate biomarker tests, such as the ExoDx™ Prostate (IntelliScore), or EPI test, have been shown to provide individualized risk assessment of clinically significant prostate cancer at initial biopsy; however, the performance in the repeat biopsy setting is not well established. METHODS: As part of a previous prospective clinical validation study evaluating the performance of the EPI test, we collected first-catch, non-DRE urine samples across 22 sites from men with at least one prior negative biopsy scheduled to undergo a repeat prostate biopsy to rule out prostate cancer. All men were 50 years or older with a PSA 2-10 ng/mL. Exosomal mRNA was extracted and expression of three genomic markers, PCA3, ERG and SPDEF was measured. The resulting EPI score was correlated with biopsy results. RESULTS: 229 men with a prior negative biopsy underwent repeat biopsies. ExoDx Prostate demonstrated good performance ruling out high-grade (Grade group 2, GG2, or higher) prostate cancer (HGPCa) using the previously validated 15.6 cut point in the initial biopsy setting. The EPI test yielded an NPV of 92% independent of other clinical features and would have avoided 26% of unnecessary biopsies while missing only five patients with HGPCa (2.1%). Furthermore, the EPI test provided additional information at a cut-point of 20 and 29.6 with an NPV of 94%, potentially delaying 35 and 61% of unnecessary biopsies, respectively. AUC curves and Net Health Benefit Analyses demonstrated superior performance of ExoDx Prostate over PSA and clinical only risk calculators, i.e. ERSPC. CONCLUSIONS: The EPI test provided good performance using the 15.6 cut-point for ruling out HGPCa / GG2 or higher in men undergoing a repeat prostate biopsy with a PSA of 2-10 ng/ml. Furthermore, the test utilizes gene expression data independent of clinical features to predict the likelihood of HGPCa / GG2 on a subsequent needle biopsy.

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness.

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4).

OBJECTIVE: To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens. METHODS: Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population. RESULTS: The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80. CONCLUSION: Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

Rapid and Short-term Extracellular Matrix-mediated In Vitro Culturing of Tumor and Nontumor Human Primary Prostate Cells From Fresh Radical Prostatectomy Tissue.

OBJECTIVE: To culture prostate cells from fresh biopsy core samples from radical prostatectomy (RP) tissue. Further, given the genetic heterogeneity of prostate cells, the ability to culture single cells from primary prostate tissue may be of importance toward enabling single-cell characterization of primary prostate tissue via molecular and cellular phenotypic biomarkers. METHODS: A total of 260 consecutive tissue samples from RPs were collected between October 2014 and January 2016, transported at 4°C in serum-free media to an off-site central laboratory, dissociated, and cultured. A culture protocol, including a proprietary extracellular matrix formulation (ECMf), was developed that supports rapid and short-term single-cell culture of primary human prostate cells derived from fresh RP samples. RESULTS: A total of 251 samples, derived from RP samples, yielded primary human tumor and nontumor prostate cells. Cultured cells on ECMf exhibit (1) survival after transport from the operating room to the off-site centralized laboratory, (2) robust (>80%) adhesion and survival, and (3) expression of different cell-type-specific markers. Cells derived from samples of increasing Gleason score exhibited a greater number of focal adhesions and more focal adhesion activation as measured by phospho-focal adhesion kinase (Y397) immunofluorescence when patient-derived cells were cultured on ECMf. Increased Ki67 immunofluorescence levels were observed in cells derived from cancerous RP tissue when compared to noncancerous RP tissue. CONCLUSION: By utilizing a unique and defined extracellular matrix protein formulation, tumor and nontumor cells derived from primary human prostate tissue can be rapidly cultured and analyzed within 72 hours after harvesting from RP tissue.

Live-single-cell phenotypic cancer biomarkers-future role in precision oncology?

The promise of precision and personalized medicine is rooted in accurate, highly sensitive, and specific disease biomarkers. This is particularly true for cancer-a disease characterized by marked tumor heterogeneity and diverse molecular signatures. Although thousands of biomarkers have been described, only a very small number have been successfully translated into clinical use. Undoubtedly, there is need for rapid, quantitative, and more cost effective biomarkers for tumor diagnosis and prognosis, to allow for better risk stratification and aid clinicians in making personalized treatment decisions. This is particularly true for cancers where specific biomarkers are either not available (e.g., renal cell carcinoma) or where current biomarkers tend to classify individuals into broad risk categories unable to accurately assess individual tumor aggressiveness and adverse pathology potential (e.g., prostate cancer), thereby leading to problems of over-diagnosis and over-treatment of indolent cancer and under-treatment of aggressive cancer. This perspective highlights an emerging class of cancer biomarkers-live-single-cell phenotypic biomarkers, as compared to genomic biomarkers, and their potential application for cancer diagnosis, risk-stratification, and prognosis.

The evaluation and treatment of prostate-related LUTS in the primary care setting: the next STEP.

The primary care provider is in an optimal position to identify and care for the patient affected by prostate-related lower urinary tract symptoms (BPH-LUTS). This assessment can be readily accomplished with a good history and only a few simple, in-office tests. In some instances, the primary care provider may believe that they do not have the experience, comfort level or time to perform an adequate evaluation of the problem. Therefore, in this article, we describe the function of the lower urinary tract, as well as the rationale behind the medical history, examination and tests needed in order to diagnose and treat, or refer, the patient presenting with BPH-LUTS. We will also update the literature-based STEP (simplified treatment of the enlarged prostate) approach, which was initially published in 2009, to include recently described therapeutic options.

The mast cell in interstitial cystitis: role in pathophysiology and pathogenesis.

Current evidence from clinical and laboratory studies confirms that mast cells play a central role in the pathogenesis and pathophysiology of interstitial cystitis (IC). In this article, we focus on the role of the mast cell in IC and examine the ways in which mast cells and other pathophysiologic mechanisms are interrelated in this disease. Identifying the patients with IC who have mast cell proliferation and activation will enable us to address this aspect of disease pathophysiology in these individuals with targeted pharmacotherapy to inhibit mast cell activation and mediator release.

Epidemiologic issues in interstitial cystitis.

As a result of variations in disease definition and diagnostic criteria for interstitial cystitis (IC), the performance of epidemiologic studies has been challenging. Initial prevalence studies used physician-confirmed diagnoses of IC; more recent studies, which have incorporated the use of patient responses to validated symptom questionnaires, indicate that the true prevalence of IC is much greater than the early studies suggested. Over the last decade, the recognized prevalence of IC has increased, and it is consistently greater among women compared with men. The most recent estimates indicate that at > or = 197 of every 100,000 women and > or = 41 of every 100,000 men in the United States are affected by IC. Because IC is substantially underdiagnosed, its actual prevalence may be much higher. Indeed, the disease may affect as many as 1 in 4 to 5 women and 1 in 20 men.