- Researchers have developed a simple new tool that can identify the risk level of metastatic prostate cancer patients earlier.
- At the time of diagnosis, the test can predict how well the patient will respond to treatment for metastatic hormone-sensitive prostate cancer (mHSPC)
- Importantly, it can identify high-risk patients who are more likely to have a poor response to treatment.
A recent abstract published in
In a nutshell, the innovative system predicts early favorable prostate-specific antigen (PSA) response in people with mHSPC who have been prescribed an androgen receptor pathway inhibitor (ARPI).
ARPI are a mainstay in the treatment of prostate cancer, and are being used increasingly often.
Although modern treatment protocols are consistently improved and updated, there is still room for improvement.
“Prostate cancer is the most common solid tumor in men, and in both the United Kingdom and the United States, it is the second leading cause of death from cancer in men,” Michael Morris, MD, told Medical News Today.
Morris, a genitourinary medical oncologist from Memorial Sloan Kettering Cancer Center in New York, was not involved in the study. He also explained that “the global prevalence of prostate cancer is rising.”
The researchers had access to patient data from three prostate cancer drug trials:
Of these cohorts, 1,030 participants were used to train a multivariable logistic regression model, and data from the remaining 688 was used for internal validation.
Once the model was finalized and “locked,” they further validated its performance using data from 540 participants in the enzalutamide arm of the
Their final analysis concluded that the tool can accurately predict early favorable PSA responses.
MNT contacted the corresponding author of the study, Daniel Spratt, MD. He is a board-certified, international expert in the management of prostate cancer, based at University Hospitals in Cleveland, OH.
“Currently, we use very dull instruments to risk-stratify patients with metastatic prostate cancer,” he explained. “Most commonly, we dichotomize patients into what is called low-volume and high-volume disease.”
Although this is standard practice, it is far from ideal. “What we and others have repeatedly shown is that volume of disease poorly predicts response to treatment, including achieving a favorable PSA response to undetectable or very low levels,” Spratt told MNT.
“Our tool enables providers and patients to understand their individual probability of achieving a favorable response to standard hormone therapy with an ARPI, and if modifications to that treatment plan are needed.”
MNT also contacted former cancer researcher and Music Beats Cancer founder Mona S. Jhaveri, PhD, who was not involved in the study.
“It would be highly valuable to predict early PSA response because its values are strongly associated with progression-free overall survival,” she explained. “If we could predict response early, we could personalize treatment, enabling more targeted and safer treatments.”
In agreement, Morris told MNT, “being able to predict who will respond well and who will not respond well to standard approaches allows for the individualized treatment of patients,” he explained.
“Patients at high risk for incomplete or non-durable responses could be treated with more intensive therapies, and those with more favorable predicted responses to conventional treatments could be treated with those.” concluded Morris.
Spratt also hopes that the tool will help clinicians personalize treatments and manage the balancing act between “oncologic efficacy and toxicity from therapy.”
Spratt told MNT that they have already begun the next phase of research to fine their tool.
“We have already begun validation of our model in another phase III randomized trial, and aim to then validate it in real-world data,” he said.
Jhaveri explained to MNT that “prostate cancer is not a solved problem, especially in advanced disease. Innovations that help enable earlier and more precise decision-making are urgently needed.”
Spratt believes that, “Collectively, we believe this will provide the strength in evidence to implement the tool in future clinical trials.”
