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Hello, my name is Rana McKay and I'm a GU Medical Oncologist at the University of California in San Diego. I'm excited to be with you today to discuss how we identify patients with localized prostate cancer, who are at increased risk of developing metastatic disease.
Prostate cancer is a common malignancy. It is the most common cancer among men, with an increased incidence most recently. Additionally, it's the second leading cause of cancer death among men in the United States. The majority of patients with prostate cancer present with localized disease, but of those patients with localized disease, about 15% of them present with high-risk features. And we'll talk about what some of those high-risk features are. Those high-risk features can be associated with an increased risk of prostate cancer-specific mortality.
The factors that impact outcome largely include stage, that's AJCC staging, Gleason score and other pathologic features, PSA, and molecular features.
High-risk prostate cancer is associated with negative outcomes. And while the majority of patients are cured with definitive local therapy or salvage therapy, a subset of patients go on and develop metastatic disease and lethal prostate cancer. You can see here that the prostate - the 15-year prostate cancer-specific mortality for patients with a PSA of greater than 20 is at 22%, Gleason score 8 to 10 at 34%, clinical T3 disease at 38%. And if we aggregate these data into the NCCN high-risk definition, it's 19%.
With regards to staging, that's largely determined with rectal exam and more recently multiparametric MRI, and PSMA PET imaging is also being integrated to identify nodal disease. For those patients that have extraprostatic disease or node positive disease, rates of prostate cancer-specific mortality certainly increase over time. There's other pathologic features that can also impact outcomes. Traditionally, Gleason score and higher Gleason score is associated with worse outcomes, at least in 8, 9, 10 disease. And the more recently described entity intraductal carcinoma has been found to be associated with more aggressive disease features.
Here is an analysis from the V.A. database, looking at patients with intraductal carcinoma of the prostate. And you can see that those patients that have intraductal carcinoma are associated with an increased risk of biochemical recurrence and increased risk of metastasis when we look at their outcomes for their prostate cancer.
There are novel tools that are being developed to help with risk stratification in the localized setting. The Artera MMAI algorithm utilizes pathologic features and computer learning to generate a AI score that can be prognostic of distant metastases at 5 years, 10 years, and prostate cancer-specific mortality overall survival. And here you can see in this large data set from 5 phase 3 trials, the utility of this algorithm in regards to just normal clinical features.
The NCCN risk stratification parameters are very useful. They're probably what's largely used in clinical practice, with high-risk disease being defined as those patients who have T3 disease, grade 4 or 5 disease, or a PSA of greater than 20.
Other tools are also being utilized to help with risk stratification. These tools integrate clinical parameters, such as the NCCN tool, which we've already talked about, but other tools include STAR-CAP, CAPRA, the MSKCC nomogram, and these tools are largely prognostic and they read out on prostate cancer-specific mortality and BCR. Artera AI prostate integrates computer learning and path AI. The gene expression scores include Decipher, Polaris, and Oncotype. Again, these are all prognostic tools to help with risk stratification.
So in summary, the majority of patients with prostate cancer present with localized disease. A subset of patients have high-risk features that are associated with an increased risk of prostate cancer death. Risk stratification largely depends on clinical parameters, including PSA, Gleason score, and stage. And lastly, additional tools that integrate molecular features and pathologic AI are being further integrated in risk stratification.
Thank you so much for being here with us today.
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