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Joseph Liao is the Kathryn Simmons Stamey Professor at Stanford University. Dr. Liao is a board-certified urologic surgeon and a NIH-funded physician scientist who is nationally recognized for his contributions in optical imaging and image-guided surgery of urological cancers, and development of urine-based precision diagnostics for bladder cancer and urinary tract infections. Fellowship-trained in endourology and minimally invasive surgery, his clinical practice focuses on care of patients with early-stage high risk urothelial and prostate cancer.Dr. Liao graduated with honors from Harvard University and earned his medical degree from Stanford School of Medicine. He completed his urology residency and clinical fellowship at UCLA Medical Center and a research fellowship at UCLA School of Engineering. He joined the faculty in the Department of Urology at Stanford in 2006 and currently serves as vice chair for academic affairs and co-director of the endourology fellowship. For 15 years, he served as the chief of urology at VA Palo Alto Health Care System, the major academic affiliate of Stanford Medicine. Dr. Liao is a member of Stanford Bio-X, Cancer Institute, Center for Artificial Intelligence in Medicine and Imaging, and Canary Center for Early Cancer Detection.Dr. Liao’s scholarship focuses on development of precision diagnostics and therapy for major urological diseases including bladder cancer, urinary tract infections, and kidney stone disease. His multidisciplinary laboratory interfaces genomics, imaging science, data science, and clinical medicine. He has served as the principal investigator on several NIH R01’s on molecular imaging, liquid biopsy, and AI-augmented surgery for bladder cancer; as well as development of integrated biosensors for rapid uropathogen identification and antimicrobial susceptibility testing. He has authored over 180 publications in top journals including Science Translational Medicine, Nature Medicine, Cell, Cancer Cell, PNAS, JAMA Surgery, and European Urology, and served as a reviewer on over 30 NIH study sections. Dr. Liao is committed to the training of next generation of physician scientists and researchers. He directs the NIH-funded K12 Urology Research at Stanford (KUReS) Career Development Program and serves as the site lead of a U2C/TL1 multi-institutional training program across Stanford, UCSF, UC Davis, and UC Berkeley, Many of his over 50 students and trainees are leaders and emerging leaders in academia and industry.Dr. Liao is an elected member of the American Society of Clinical Investigation. He is active in major national urology organizations and formerly served as the president of the Engineering and Urology Society and on the board of the directors for the Endourology Society.
1. Multimodal optical imaging of bladder cancerA major focus of my lab is to develop enhanced endoscopic imaging technologies (e.g. fluorescence, endomicroscopy, molecular imaging, computer vision, and artificial intelligence) to improve bladder tumor detection and resection. We have investigated CD47, an innate immune checkpoint, as a bladder cancer imaging and therapeutic target. We identified and validated CD47 as a promising cancer imaging target and proposed a strategy for intravesical administration of imaging agents based on therapeutic antibodies (Pan 2014). In animal models, we investigated the biodistribution and systemic toxicity of nanoparticle-labeled anti-CD47 (Pan 2017) and demonstrated that CD47-mediated near-infrared photoimmunotherapy, a novel form of targeted phototherapy, led to enhanced tumor destruction and prolonged survival (Kiss 2019). In the clinical arena, we pioneered the urological applications of confocal endomicroscopy for bladder cancer (Sonn 2009), upper tract urothelial carcinoma (Bui 2015) and prostate cancer (Lopez 2015). This optical biopsy technology enables real-time intraoperative imaging with spatial resolution comparable to histology. Finally, we are developing computer vision and artificial intelligence tools to enhance cystoscopic navigation and tumor identification. We have an ongoing effort to curate a high quality annotated cystoscopy imaging dataset of diverse bladder cancer variants and recently reported the first AI-assisted cystoscopy using convolutional neural networks for automated tumor annotation (Shkolyar 2019).2. Precision diagnostics for bladder cancer Another major focus is identification and validation of urine-based biomarkers to inform bladder cancer diagnosis, prognosis, and treatment response. Given its abundance and non-invasive nature of sample collection, urine serves as an ideal source of liquid biopsy. To overcome the diagnostic shortcomings of standard urine cytology, we have utilized high throughput sequencing technology as a discovery and diagnostic tool. We applied bulk RNA sequencing to urinary pellets for biomarker discovery and developed a diagnostic 3-marker urinary RNA panel (Sin 2017), and led a multi-center effort to validate another urinary RNA panel using an integrated microfluidic cartridge (Wallace 2019). Collaboratively with other colleagues at Stanford, we are developing an ultrasensitive targeted sequencing approach called uCAPP-Seq (urine tumor DNA Cancer Personalized Profiling by deep sequencing) for bladder cancer (Dudley 2019) and have started prospective longitudinal validation studies of these precision diagnostic platforms.3. Precision diagnostics of urinary tract infections A longstanding focus of my lab has been the development of molecular diagnostics for bacterial infections to direct evidence-based utilization of antibiotics and curtail the proliferation of multidrug resistant pathogens. We focus on urinary tract infections, the most common urological disease and healthcare-associated infection. We have made broad advances in molecular probe development for amplification-free bacterial detection, microfluidic-based antimicrobial susceptibility testing (AST), sample preparation strategies compatible with matrix effects from clinical samples, system integration, and single cell analysis. Representative works include rapid, sensitive pathogen identification through 16S rRNA targeting (Ouyang 2013, Mach 2019), validation of integrated AST in clinical samples (Altobelli 2016), and single cell analysis (Hsieh 2018, Li 2019). Most recently, we report a 30-minute sample-to-answer assay based on single-cell measurements of bacterial 16S rRNA in picoliter droplets to achieve simultaneous molecular pathogen identification and phenotypic antimicrobial susceptibility assessment (Kaushik 2021).