Goodison, Steve

Abstract Background and Objective: Bladder cancer is the 9th most diagnosed cancer worldwide with high incidences reported in Europe and the United States. Here, we evaluated the real-world performance of a commercially available multiplex immunoassay Oncuria-Detect that detects bladder cancer by simultaneously measuring a panel of 10 biomarker proteins in naturally voided urine samples. Methods: We tested prospectively collected urine samples from a real-world cohort of 1, 002 patients presenting to five USA centers, one European center and one Japanese center with hematuria. Assay results were collated with patient clinical data and a cancer diagnosis was defined by biopsy and pathology. The prevalence of bladder cancer in the cohort was 15.9%. Results: In a training set (n=684), the Oncuria-Detect assay correctly identified bladder cancer in 94/109 cases, representing a sensitivity of 91.8%, specificity of 71.6% (AUC 0.885; 95% CI 0.843 - 0.927) and with improved performance for high-grade bladder cancer and muscle-invasive bladder cancer (sensitivity 93.6% and 92.1%, respectively). In a test cohort of n =339 the AUC for detecting bladder cancer was 0.850 (95% CI 0.779-0.920). Conclusions and Implications: The multiplex Oncuria-Detect assay identified bladder cancer with high sensitivity and NPV, for low-grade and high-grade tumors alike. Oncuria-Detect’s high NPV could effectively rule out 66% of patients from requiring subsequent cystoscopy. Citation Format: Ian Pagano, Zhen Zhang, Sergei Tikhonenkov, Florence Le Calvez-Kelm, Steve Goodison, Toru Sakatani, Kaoru Murakami, Takashi Kobayashi, Howard Kim, Riko Lee, Sunao Tanaka, Charles J. Rosser, Hideki Furuya. Performance of the Oncuria-detect bladder cancer test for evaluating patients presenting with hematuria: results from a real-world clinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5919.

Abstract Network-based methods utilize protein–protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein–protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.

Abstract Up to 70% of patients with non-muscle invasive bladder cancer (NMIBC) experience disease recurrence, making it one of the most prevalent cancers in the US. The purpose of this study was to test the performance of Oncuria®-Monitor, a multiplex urinary biomarker assay for the monitoring of voided urine for recurrent bladder cancer. This cross-sectional, multicenter study included a total of 283 subjects with a history of bladder cancer. Voided urine specimens were collected prior to any procedures for multiplex analysis. The diagnostic performance of the biomarker panel was assessed using receiver operator curves (ROC) and descriptive statistical values (e.g., sensitivity and specificity) and compared with the patients’ clinical data and final diagnosis as defined by the results of cystoscopy. Oncuria®-Monitor achieved an overall sensitivity of 94.3%, specificity of 83.6% and negative predictive valuer (NPV) of 96.2% for the detection of recurrent bladder cancer. Patient summary: We conducted a large cross-sectional clinical study for detecting recurrent bladder cancer with a novel, simple urine test (Oncuria®- Monitor), which measures 10 bladder cancer associated proteins in a single voided urine and can be used to detect recurrent bladder cancer. We analyzed urines from 283 patients and demonstrated that Oncuria®-Monitor can detect recurrent bladder cancer with a high degree of accuracy, specifically in patients with high grade or high stage disease. In conclusion, the novel Oncuria®-Monitor urine test can be used to surveil patients with a history of bladder cancer. Citation Format: Zhen Zhang, Florence Le Calvez-Kelm, Makito Miyake, Ian Pagano, Takuto Shimizu, Sergei Tikhonenkov, Sunao Tanaka, Charles J Rosser, Steve Goodison, Hideki Furuya. Oncuria®-Monitor, a non-invasive test for the detection of recurrent bladder cancer: Cross sectional performance evaluation from a multicenter study [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A072.

Abstract Bladder cancer is the 9th most diagnosed cancer worldwide with higher incidences reported in Europe and the United States. Here, we evaluate the performance of a novel, commercially available multiplex immunoassay querying for 10 proteins associated with a bladder cancer diagnostic signature (Oncuria®-Detect, Nonagen Bioscience Corp, Los Angeles, CA, US) for the detection of de novo bladder cancer, in a large prospectively collected real world cohort comprised of 995 patients, across seven centers, presenting with hematuria. The results were compared with the patients’ clinical data and final diagnosis as defined by the results of cystoscopy, with a prevalence of bladder cancer of 15.9%. In the test cohort, Oncuria®-Detect detected bladder tumors in 94/109 cancers, giving overall sensitivity of 86.2% and an overall negative predictive value (NPV) of 96.7% with corresponding specificity of 80.2% with favorable performance characteristics in high grade bladder cancer and muscle-invasive bladder cancer. Patient summary: We conducted a large cross-sectional clinical study for detecting de novo bladder cancer with a novel, simple urine test (Oncuria®-Detect), which measures 10 bladder cancer associated proteins in a single voided urine sample. We analyzed urines from 995 patients and demonstrated that the new urine test can detect bladder cancer with a high degree of accuracy, specifically in patients with high grade or high stage disease. In conclusion, the novel Oncuria®-Detect urine test can be used to help detect new bladder cancers. Citation Format: Ian Pagano, Sergei Tikhonenkov, Florence Le Calvez-Kelm, Steve Goodison, Zhen Zhang, Toru Sakatani, Kaoru Murakami, Howard Kim, Riko Lee, Sunao Tanaka, Charles J Rosser, Hideki Furuya. Performance of the Oncuria®-Detect test for the evaluation of patients presenting with hematuria: Results from a real world clinical setting [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A071.

Abstract Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer. Significance: We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Background: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. Methods: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. Results: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Purpose: Bladder cancer (BCa) is one of the most common cancer types worldwide and is characterized by a high rate of recurrence. In previous studies, we and others have described the functional influence of plasminogen activator inhibitor-1 (PAI1) in bladder cancer development. While polymorphisms in PAI1 have been associated with increased risk and worsened prognosis in some cancers, the mutational status of PAI1 in human bladder tumors has not been well defined. Methods: In this study, we evaluated the mutational status of PAI1 in a series of independent cohorts, comprised of a total of 660 subjects. Results: Sequencing analyses identified two clinically relevant 3′ untranslated region (UTR) single nucleotide polymorphisms (SNPs) in PAI1 (rs7242; rs1050813). Somatic SNP rs7242 was present in human BCa cohorts (overall incidence of 72%; 62% in Caucasians and 72% in Asians). In contrast, the overall incidence of germline SNP rs1050813 was 18% (39% in Caucasians and 6% in Asians). Furthermore, Caucasian patients with at least one of the described SNPs had worse recurrence-free survival and overall survival (p = 0.03 and p = 0.03, respectively). In vitro functional studies demonstrated that SNP rs7242 increased the anti-apoptotic effect of PAI1, and SNP rs1050813 was related to a loss of contact inhibition associated with cellular proliferation when compared to wild type. Conclusion: Further investigation of the prevalence and potential downstream influence of these SNPs in bladder cancer is warranted.

Abstract Summary Quantifying pairwise sequence similarities is a key step in metagenomics studies. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. In this article, we describe the development of a new method, referred to as AsMac that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and developed an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method. Availability and implementation The open-source software for the proposed method and trained neural-network models for some commonly used metagenomics marker genes were developed and are freely available at www.acsu.buffalo.edu/~yijunsun/lab/AsMac.html. Supplementary information Supplementary data are available at Bioinformatics online.

Bladder cancer is a biologically heterogeneous disease with variable clinical presentations, outcomes and responses to therapy. Thus, the clinical utility of single biomarkers for the detection and prediction of biological behavior of bladder cancer is limited. We have previously identified and validated a bladder cancer diagnostic signature composed of 10 biomarkers, which has been incorporated into a multiplex immunoassay bladder cancer test, Oncuria™. In this study, we evaluate whether these 10 biomarkers can assist in the prediction of bladder cancer clinical outcomes. Tumor gene expression and patient survival data from bladder cancer cases from The Cancer Genome Atlas (TCGA) were analyzed. Alignment between the mRNA expression of 10 biomarkers and the TCGA 2017 subtype classification was assessed. Kaplan–Meier analysis of multiple gene expression datasets indicated that high expression of the combined 10 biomarkers correlated with a significant reduction in overall survival. The analysis of three independent, publicly available gene expression datasets confirmed that multiplex prognostic models outperformed single biomarkers. In total, 8 of the 10 biomarkers from the Oncuria™ test were significantly associated with either luminal or basal molecular subtypes, and thus, the test has the potential to assist in the prediction of clinical outcome.

Background: The Oncuria™ urine test for the detection of bladder cancer measures a multiplex protein signature. In this study, we investigated the influence of urinary cellularity, protein, and hematuria on the performance of the Oncuria™ test in an ex vivo experimental model. Materials and Methods: Pooled urine from healthy subjects was spiked with cultured benign (UROtsa) or malignant cells (T24), cellular proteins, or whole blood. The resulting samples were analyzed using the Oncuria™ test following the manufacturer’s instructions. Results: Urine samples obtained from healthy subjects were negative for bladder cancer by Oncuria™ test criteria. The majority of the manipulated conditions did not result in a false-positive test. The addition of whole blood (high concentration) did result in a false-positive result, but this was abrogated by sample centrifugation prior to analysis. The addition of cellular proteins (high concentration) resulted in a positive Oncuria™ test, and this was unaffected by pre-analysis sample centrifugation. Conclusions: The Oncuria™ multiplex test performed well in the ex vivo experimental model and shows promise for clinical application. The identification of patients who require additional clinical evaluation could reduce the need to subject patients who do not have bladder cancer to frequent, uncomfortable and expensive cystoscopic examinations, thus benefiting both patients and the healthcare system.