CLEVELAND - A new study published in Nature Communications provides a framework for researching whether earlier, model-guided treatment intensification can meaningfully improve survival for patients with aggressive disease.
"Early decline in prostate-specific antigen (PSA) to very low levels is one of the strongest predictors of long-term survival in metastatic prostate cancer. However, clinicians currently have to wait up to six months after starting therapy to see whether a patient achieves this favorable response. For patients who do not respond well, this delay may allow the cancer to progress and become more resistant to treatment," said Soumyajit Roy, MD , a radiation oncologist at UH Seidman Cancer Center and first author of the study.
Because existing clinical risk stratification tools - such as disease volume or metastatic burden - are relatively imprecise, there has been an unmet need for a reliable, easy-to-use tool that can risk stratify patients earlier, before that critical six-month window closes. Researchers wanted to determine whether it is possible to predict early treatment response at the time of diagnosis for men with metastatic hormone-sensitive prostate cancer (mHSPC) who are treated with modern androgen receptor pathway inhibitors (ARPIs), which are now standard of care worldwide.
This study provides one of the first rigorously validated tools that can predict early biochemical response before treatment outcomes are known in mHSPC.
"The significance lies in shifting prostate cancer care from a reactive approach - waiting to see who fails therapy to a proactive, personalized strategy. By identifying patients who are unlikely to achieve an early favorable PSA response, clinicians may be able to intervene sooner, consider treatment intensification, or prioritize enrollment in clinical trials," said senior author Daniel Spratt, MD , Vincent K. Smith Chair of Radiation Oncology, UH Seidman Cancer Center and Associate Chief Scientific Officer, UH CMC. Additionally, the model outperformed commonly used single risk factors, such as PSA alone or metastatic volume, highlighting the importance of integrating multiple clinical variables rather than relying on any one measure.
This model could help clinicians:
• Identify high-risk patients at diagnosis who are less likely to respond optimally to standard therapy
• Guide early treatment discussions, including whether additional therapies or closer monitoring may be appropriate
• Improve shared decision-making, allowing patients to better understand their expected response to treatment
• Optimize clinical trial design, by enriching studies with patients most likely to benefit from early treatment intensification or novel strategies
The next critical step is further validation of the model in real-world clinical settings and within ongoing clinical trials. This will determine how best to integrate the model into routine practice and whether its use can directly improve patient outcomes. Researchers are also interested in enhancing the model by incorporating additional biomarkers, such as genomic, molecular, or advanced imaging data, to further refine risk prediction.
Finally, this work provides a framework for studying whether earlier, model-guided treatment intensification can meaningfully improve survival for patients with aggressive disease.
