Traditional prostate cancer features fail to predict hormone therapy benefit

A new study led by investigators at the UCLA Health Jonsson Comprehensive Cancer Center found that while traditional pathological features used to classify aggressive prostate cancer -such as high-grade disease, cancer extending outside the prostate and cancer involving the seminal vesicles- after surgery can help identify patients who are more likely to have worse outcomes, they do not predict who will benefit more from adding hormone therapy to postoperative radiation. 

The findings, published in European Urology, were led by Dr. Amar Kishan, professor and executive vice chair of radiation oncology at the David Geffen School of Medicine at UCLA, and highlight the need for molecular biomarkers that can better predict treatment response and personalize treatment based on tumor biology. 

Why it matters

After prostate cancer surgery, some patients receive radiation therapy if there is concern that their cancer may return. Doctors may add hormone therapy, which reduces the levels of hormones that can fuel prostate cancer growth, to improve outcomes for certain patients.

However, hormone therapy can cause side effects, including fatigue, hot flashes, sexual dysfunction, bone loss and metabolic changes. Identifying which patients are most likely to benefit is important to ensure patients receive effective treatment while avoiding unnecessary side effects.

Doctors commonly use features found in the prostate tissue removed during surgery to estimate how aggressive a patient's disease may be. These features are known to predict outcomes, but it has remained unclear whether they can identify patients who are more likely to benefit from adding hormone therapy.

What the study did

Researchers analyzed individual patient data from five phase 3 randomized clinical trials that compared postoperative radiation therapy alone with radiation therapy combined with hormone therapy.

The analysis included 4,781 patients who had undergone prostate cancer surgery and later received radiation therapy. The researchers developed an adverse feature count score based on four high-risk pathological findings:

• High-grade disease (Grade Group 4-5)
• Cancer that had spread into the seminal vesicles
• Cancer that extended outside the prostate
• Positive surgical margins, meaning cancer cells were found at the edge of the removed tissue

Patients received a score from zero to four based on the number of adverse features present. The researchers then evaluated whether this score influenced the benefit of adding hormone therapy for two key outcomes, overall survival and metastasis-free survival.

What they found

The researchers confirmed that patients with more adverse pathological features had worse outcomes. Patients with a higher adverse feature count had an increased risk of death and cancer spreading to other parts of the body.

However, the number of adverse features did not predict whether a patient would benefit more from adding hormone therapy to radiation therapy.

Even patients with multiple high-risk features, including high-grade disease and seminal vesicle invasion, did not experience a greater improvement in overall survival or metastasis-free survival from adding hormone therapy compared with patients with fewer risk factors. 

The findings were consistent among patients with a prostate-specific antigen (PSA) level of 0.5 ng/mL or less before radiation, a group in which previous research found limited overall benefit from adding hormone therapy. 

The impact

The study shows that traditional pathological features are useful for identifying patients with a higher risk of recurrence but do not indicate who will respond best to hormone therapy.

These findings highlight an important distinction between predicting risk and predicting treatment benefit. While pathology helps us understand which cancers are more aggressive, we need biomarkers that can tell us which patients are most likely to benefit from a specific therapy. By identifying which patients truly benefit from additional treatment, more personalized approaches could help improve outcomes while reducing unnecessary treatment-related side effects."

Dr. Amar Kishan, co-director of the cancer molecular imaging, nanotechnology and theranostics program, UCLA Health Jonsson Comprehensive Cancer Center

The research team

The study's senior author is Dr. Daniel Spratt from the University Hospitals Seidman Cancer Center and Case Western Reserve University. Other UCLA authors are: Tahmineh Romero-Kalbasi, Dr. Michael Steinberg, Dr. Luca Valle, Dr. Kekoa Taparra, Dr. Matthew J. Farrell, Dr. Matthew Rettig, Dr. Adam Singer, Dr. Nikita Baclig, Dr. Robert Reiter, Dr. Scott Eggener, Dr. Wayne Brisbane and Dr. Nicholas Nickols.