Dr. Cookson on Understanding Predictive Features in the Management of Prostate Cancer

Michael S. Cookson, MD, MMHC, professor, chairman of urology, University of Oklahoma College of Medicine, chief, Urology, Stephenson Cancer Center, discusses how understanding the relationship between key predictive factors and disease progression can help better inform the overall management of prostate cancer.

It is imperative to understand a patient's natural history when determining a treatment approach, as this can often predict their disease course, Cookson begins. The typical disease course for patients in this space is biochemical progression followed by local recurrence, metastatic disease, and possibly death from disease, he says. However, not all patients experience this progression.

An early, retrospective study conducted at Johns Hopkins Medical Institutions aimed to better understand and characterize the time course of disease progression in patients who received radical prostatectomy that led to a subsequent biochemical recurrence, Cookson expands. The study found that 34% of patients with prostate cancer developed metastatic disease, Cookson states. Within this group, the median time from biochemical recurrence to metastatic disease was 8 years, and the median time from metastasis to death was 5 years.

Additionally, a patient's comorbidities can also influence treatment outcomes, Cookson continues. Previous research from investigators at the Mayo Clinic demonstrated that patients with biochemical recurrence in an all-comer population had a low chance of death from prostate cancer at 15 years, Cookson says, adding that a percentage of patients in this population were low risk. This indicates that biochemical-only disease does not always lead to poor responses, a worsening in patient outcomes, or death, Cookson explains.

The severity of a patient's cancer grade is particularly critical in determining eligibility for surgery, Cookson says. Clinicians should pay close attention to patients who have early biochemical recurrence or who never achieve undetectable disease, as they have a high risk of recurrence, he notes. Other factors, such as tumor doubling times, are more relevant if considering radiation therapy, he states.

According to multivariate models, factors such as rapid tumor doubling time, early recurrence, and high-grade disease, are predictive of higher-risk disease, Cookson adds. Conversely, patients are more likely to undergo a protracted disease course if these features are not present, Cookson states. Going forward, emerging artificial intelligence models could incorporate or replace other traditional clinical factors, Cookson predicts. Overall, better predictors of patient outcomes are on the horizon, Cookson concludes.

Editor’s Note: Dr. Cookson reports serving as a consultant or in an advisory role for Astellas Pharma, Bayer, Ferring, Genomic Health, Janssen Biotech, MDxHealth, Merck, Myovant Sciences, Precision Biopsy, TesoRX Pharmaceuticals; he received honoraria from AstraZeneca/MedImmune, Clovis Oncology, FerGene, Merck, Myovant Sciences.