Tanaka, Sunao

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 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 Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high‐throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi‐marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.