Introduction: Recently, we reported that OncuriaTM, a multiplex urinalysis test, could be predictive of BCG treatment response. Furthermore, high-grade BCG unresponsive bladder cancer have limited treatment options. Such patients are offered either radical cystectomy or systemic therapy. One of the systemic therapies include immune-oncology agents, e.g., PD-1 or PD-L1 inhibitors. In this study, we tested the performance of OncuriaTM in a BCG unresponsive cohort to determine if it could predict response to a PDL1 inhibitor. Methods: OncuriaTM data was evaluated in voided urine samples obtained from a prospectively collected cohort of 18 subjects who have BCG-unresponsive NMIBC with treatment of atezolizumab monotherapy or atezolizumab BCG combination (provided by Genentech, NCT02792192). The urine samples were collected prior to treatment in both arms. The OncuriaTM test, which measures 10 cancer-associated biomarkers was performed in an independent clinical laboratory. Predictive models were previously developed using supervised learning and cross-validation analyses. Model performance was validated using ROC curves. Results: Pre-treatment urinary concentrations of MMP9, VEGFA, CA9, SDC1, PAI1, APOE, A1AT, ANG and MMP10 were increased in patients who developed disease recurrence. A combinatorial predictive model of treatment outcome achieved sensitivity and specificity of >90%. Conclusions: Previous pilot study found that monitoring the urinary levels of a cancer-associated biomarker panel enabled the discrimination of patients who did not respond to intravesical BCG therapy. In this study, we noted the performance of OncuriaTM for the prediction of systemic PDL1 inhibitor treatment response. A limitation of this study includes its small sample size. With further study, the multiplex OncuriaTM test may be applicable for the clinical evaluation of bladder cancer patients who have not previously responded to intravesical BCG treatment and is considering systemic immune-oncology options. SOURCE OF Funding: NIH/NCI R21CA263230, NIH/NCI R01CA198887
