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The goals of this Center for Benign Urological Diseases are: 1) to create and maintain an environment that supports important and innovative research in the field of benign urology by focusing on a Scientific Research Project examining “Leukocyte phenotypes associated with BPH progression,” 2) to educate and inform young scientists and physicians about BPH, and 3) to develop new interactive projects and collaborations involving other groups in the benign urology research community. The program brings together expertise in basic science, bioinformatics, and clinical urology to apply new technologies in the field of benign urologic research. The research team includes members from the benign and malignant urology fields as well as from non-urologic cancers. Benign prostatic hyperplasia (BPH) and associated lower urinary tract symptoms (LUTS) are highly prevalent and will become increasingly more frequent with an aging demographic. Approximately one-third of BPH patients are resistant to current medical therapy, and a further third of initial responders (around 10% of the total patient population) subsequently develop therapy resistance after an initial positive response. So, for around 40% of the patient population, there is no effective medical therapy, leaving surgery as the sole treatment option. Approximately 120,000 surgeries are performed annually in the United States to treat men who are resistant to the available medical interventions. Many of these are elderly patients often with significant co-morbidities who are often not ideal surgical candidates. BPH is closely associated with pro-inflammatory co-morbidities. However, the characteristics and pathways linking immune/inflammatory changes to BPH progression are unclear. This study will utilize single-cell (sc)RNA-seq to fully characterize the leukocyte population in patients with small prostates and low IPSS scores vs. those with large prostates, high symptom scores, and progression to surgery specifically for a BPH indication. This will provide a comprehensive picture of the cells that are present and will allow for the application of bioinformatics approaches to define the extracellular signaling pathways that are activated in these inflammatory cells as the disease progresses.

A complete analysis of gene expression profiles in prostatic leukocytes will allow a deeper understanding of their actions and interactions with the stromal and epithelial cells within the prostate. Given the large number of new agents becoming available to target specific components of the immune/inflammatory network, the ability to start to track the interactions between the leukocytes in the prostate should also elucidate new therapeutic options. The central hypothesis of this proposal is that in BPH patients there will be a change in the profile of gene expression in intraprostatic leukocytes as the prostate grows and BPH progresses. A detailed understanding of this process will inform the development of new therapeutic options helping to personalize BPH treatment. scRNA-seq will define specific subpopulations of leukocytes and the range of signaling molecules that they produce in BPH patients, elucidating chemokine/cytokine networks and identifying potential new therapeutic targets. We will generate a data set that will identify key leukocyte subpopulations and important pathways between them. These data will support future studies to investigate changes in the status of immune/inflammatory cells as BPH progresses. They will also be relevant to other causes of LUTS and benign urologic diseases with an inflammatory component, as well as to urologic malignancies. Two Specific Aims will be pursued by a multidisciplinary team. Aim 1: Define the profile of leukocytes and their changes during BPH progression based upon single-cell RNA-seq analysis. We will apply scRNA-seq to human BPH leukocytes to establish gene expression profiles in these cells and to identify the specific cell clusters present in the gland. Aim 2: Discover cellular clusters that possess distinct pathway topologies for key BPH pathways. We will apply heterogeneous Bayesian graphical models (HBGMs) to analyze scRNA-seq data to discover cell clusters that are estimated to have unique and different pathway topologies as sets of connected edges between genes and test these predicted changes in clinical samples.