Using adaptive genetic algorithms combined with high sensitivity single cell-based technology to detect bladder cancer in urine and provide a potential noninvasive marker for response to anti-PD1 immunotherapy.

•The objective of this study was to use adaptive genetic algorithms (AGA) in combination with single-cell flow cytometry technology to develop a noninvasive test to detect bladder cancer.•Fifty high grade, cystoscopy confirmed, superficial bladder cancer patients, and 15 healthy donor early morning urine samples were collected in an optimized urine collection media.•These samples were then used to develop an assay to distinguish healthy from cancer patients’ urine using AGA in combination with single-cell flow cytometry technology.•The resulting prediction model (biomarker) showed 98% sensitivity and 87% specificity with a high area under the ROC value (90%).