Hyung Lae Kim

Participating Sites and Patient Enrollment.

Characteristics of Urine Samples Used for Development and Validation.

Purpose:More than 50% of patients with non–muscle-invasive bladder cancer experience recurrence, requiring lifelong surveillance with repeated cystoscopy. Given the invasive nature and cost of cystoscopy, accurate noninvasive tools are needed to support risk-adapted monitoring. We evaluated the ability of Oncuria-Monitor, a multiplex urine biomarker assay, to detect recurrent bladder cancer during surveillance. Patients and Methods:Between February 2017 and August 2020, six medical centers in the United States and Japan prospectively enrolled 300 patients with a history of bladder cancer, generating 1,248 serial urine samples. Participants were divided into training and validation cohorts. At each surveillance visit over 2 years, urine samples were analyzed in a blinded manner using Oncuria-Monitor and BladderChek, alongside urine cytology. Test performance was compared with cystoscopy and histopathology-confirmed recurrence. Results:Recurrent bladder cancer was identified in 31% (93/300) of participants, with 143 total recurrences during follow-up, including 90 tumors in the validation cohort. In the validation cohort, Oncuria-Monitor achieved a sensitivity of 85.6% [95% confidence interval (CI), 78.1%–92.2%] and a negative predictive value (NPV) of 93% (95% CI, 89.2%–96.4%). In comparison, BladderChek demonstrated a sensitivity of 20.0% and an NPV of 88%, whereas urine cytology showed a sensitivity of 36.9% and an NPV of 91.6%. The number needed to evaluate to detect one recurrence was 3 for both cystoscopy and Oncuria-Monitor, compared with 15 for BladderChek and 8 for cytology. Conclusions:In this large prospective longitudinal study, Oncuria-Monitor demonstrated clinically actionable performance, enabling a rule-out strategy that could safely reduce cystoscopy in approximately 25% of surveillance visits. These findings support a paradigm shift toward biomarker-guided, risk-adapted surveillance in bladder cancer that reduces unnecessary invasive procedures while maintaining oncologic safety.

Representativeness of Study Participants.

Specificities of Oncuria-Monitor, BladderChekTM and Cytology in Benign Urological Conditions.

Purpose: More than 50% of patients with non–muscle-invasive bladder cancer experience recurrence, requiring lifelong surveillance with repeated cystoscopy. Given the invasive nature and cost of cystoscopy, accurate noninvasive tools are needed to support risk-adapted monitoring. We evaluated the ability of Oncuria-Monitor, a multiplex urine biomarker assay, to detect recurrent bladder cancer during surveillance. Patients and Methods: Between February 2017 and August 2020, six medical centers in the United States and Japan prospectively enrolled 300 patients with a history of bladder cancer, generating 1,248 serial urine samples. Participants were divided into training and validation cohorts. At each surveillance visit over 2 years, urine samples were analyzed in a blinded manner using Oncuria-Monitor and BladderChek, alongside urine cytology. Test performance was compared with cystoscopy and histopathology-confirmed recurrence. Results: Recurrent bladder cancer was identified in 31% (93/300) of participants, with 143 total recurrences during follow-up, including 90 tumors in the validation cohort. In the validation cohort, Oncuria-Monitor achieved a sensitivity of 85.6% [95% confidence interval (CI), 78.1%–92.2%] and a negative predictive value (NPV) of 93% (95% CI, 89.2%–96.4%). In comparison, BladderChek demonstrated a sensitivity of 20.0% and an NPV of 88%, whereas urine cytology showed a sensitivity of 36.9% and an NPV of 91.6%. The number needed to evaluate to detect one recurrence was 3 for both cystoscopy and Oncuria-Monitor, compared with 15 for BladderChek and 8 for cytology. Conclusions: In this large prospective longitudinal study, Oncuria-Monitor demonstrated clinically actionable performance, enabling a rule-out strategy that could safely reduce cystoscopy in approximately 25% of surveillance visits. These findings support a paradigm shift toward biomarker-guided, risk-adapted surveillance in bladder cancer that reduces unnecessary invasive procedures while maintaining oncologic safety.

Immunotherapy targeting CD4 + FoxP3 + regulatory T cells (Tregs) through CD4 + lymphocyte depletion is being investigated as cancer treatment. This study examines the efficacy and limitations of CD4 depletion across various cancer models. We examined CD4 depletion in syngeneic mouse tumor models including B16 melanoma, Renca kidney cancer, and Hepa1-6 hepatocellular carcinoma. The study explored the relationship between MHC II expression and tumor growth, immune response, and survival. In mouse models, CD4 depletion suppressed B16 and Renca tumor growth but accelerated Hepa1-6 tumors. Hepa1-6 had high MHC II expression while the other two lines had low MHC II expression. Manipulation of MHC II expression in Hepa1-6 and Renca cell lines confirmed the mechanistic importance of MHC II in generating a CD4-based antitumor immune response. Analysis of the TCGA dataset supports the relevance of this mechanism in patients. In tumors with high MHC II and low MHC I expression, increased CD4 levels correlated with improved survival. CD4 depletion can suppress tumor growth or promote tumor growth, depending on tumor MHC expression status. MHC status may identify tumors where intratumor CD4 may be a better early-response marker than CD8.

Background: Multiparametric MRI (mpMRI) as a non-invasive imaging tool is important in prostate cancer (PCa) detection and localization. Combined with radiomics analysis, features extracted from mpMRI have been utilized to predict PCa aggressiveness. T2 mapping provides quantitative information in PCa diagnoses but is not routinely available in clinical practice. Previous work from our group developed a deep learning-based method to estimate T2 maps from clinically acquired T1- and T2-weighted images. This study aims to evaluate the added value of the estimated T2 map by combining it with conventional T2-weighted images for detecting clinically significant PCa (csPCa). Methods: An amount of 76 peripheral zone prostate lesions, including clinically significant and insignificant cases, were retrospectively analyzed. Radiomic features were extracted from conventional T2-weighted images and deep learning-estimated T2 maps, followed by feature selection and model development using five-fold cross-validation. Logistic regression and Gaussian Process classifiers were employed to develop the prediction models, with performance evaluated by area under the curve (AUC) and accuracy metrics. Results: The model incorporating features from both T2-weighted images and estimated T2 maps achieved an AUC of 0.803, significantly outperforming the model based solely on T2-weighted image features (AUC of 0.700, p = 0.048). Conclusions: Radiomics features extracted from deep learning-estimated T2 maps provide additional quantitative information that improves the prediction of peripheral zone csPCa aggressiveness, potentially enhancing risk stratification in non-invasive PCa diagnostics.