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

Abstract Objective To use adaptive genetic algorithms (AGA) in combination with single-cell flow cytometry technology to develop a noninvasive test to detect bladder cancer. Materials and Methods 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. Cell recovery and test performance were verified based on cystoscopy and histology for both bladder cancer determination and PD-L1 status. Results Bladder cancer patients had a significantly higher percentage of white blood cells with substantial PD-L1 expression (P Conclusions Using single-cell technology and machine learning; we developed a new assay to distinguish bladder cancer from healthy patients. Future studies are planned to validate this assay.