Paul Nguyen

About Paul Nguyen

Identification and integration of clinical features, genetic factors, and biomarkers to optimize prostate cancer risk stratification and treatment selection

One of the most pressing dilemmas in the care of patients with prostate cancer is the ability to distinguish indolent from aggressive disease. Most of the currently available clinical tools such as PSA, cancer stage and Gleason score rely exclusively on a small number of standard clinical parameters, and cannot always reliably distinguish patients who need treatment from those who do not. As a result, a majority of patients diagnosed with prostate cancer choose radical treatments and suffer from associated side effects, even though only a small minority would have died from their disease without treatment.

However, considering the complexity of the disease, it is important to note that no single marker or diagnostic modality will likely account for all of the variability in prostate cancer outcome. In this proposal, Dr. Nguyen proposes to combine multiple markers of disease outcome into a single prognostic model to achieve maximum predictive accuracy. The overall goal of Dr. Nguyen’s efforts is to identify and integrate underlying genetic differences (polymorphisms), serum biomarkers, imaging characteristics and novel clinical factors to enhance the predictive ability of the current tools. He will study prostate cancer patient blood and tissue samples to identify biological and clinical predictors of outcome.

For biological predictors, Dr. Nguyen and his team will examine the biomarkers and genetic changes relevant to the link between obesity and aggressive prostate cancer. For clinical predictors, Dr. Nguyen will evaluate the prognostic value of clinical and MRI-based predictors of prostate cancer outcome. These factors will ultimately be tested in a large clinical cohort to determine their combined prognostic ability. Therefore, Dr. Nguyen’s research will potentially provide a single unified system that integrates multiple types of prognostic information. These results will ultimately allow patients to understand their risk of cancer recurrence with greater certainty, and make better treatment choices.