Survival and response to treatment in patients with advanced prostate cancer can be predicted by assessing changes in the number of circulating tumour cells (CTCs), according to an Article published Online First and in the March issue of The Lancet Oncology.

Prostate cancer is one of the most common types of cancer to affect men, particularly in the USA and Europe. It is usually associated with individuals over the age of 50 years. When hormonal therapy ceases to be effective, a patient is deemed to have castration-resistant prostate cancer. The progress of such disease is very difficult to track, as is the prediction of how patients will respond to chemotherapy.

Professor Howard Scher (Memorial Sloan Kettering Cancer Center, New York City, USA) and colleagues assessed the association between CTC number (both before and after treatment) and survival, and also evaluated other factors such as changes in the prostate-specific antigen (PSA) and baseline lactate dehydrogenase (LDH), in 164 patients embarking on first-line chemotherapy regimens. CTC number was determined using the US Food and Drug Administration-approved Cell Search™ (Veridex) system.

The researchers found that, before treatment, high values of CTC number and PSA were associated with increased risk of death. However, after treatment, as assessed at 4, 8, and 12 weeks, changes in CTC number were strongly associated with risk, whereas changes in PSA were only marginally associated. The study confirms that pre-treatment CTC number can be used to predict survival of patients starting first-line chemotherapy, and that it can also be used to monitor disease status and response to treatment. It is more predictive than PSA both before and after treatment.

“CTC number … can be used to monitor disease status and might be useful as an intermediate endpoint of survival in clinical trials”, said Scher. “Use as an intermediate or surrogate endpoint for survival could shorten the timeline for drug approval”, although he notes that “several prospective trials are needed to generate evidence to guide the use of biomarkers”. He added that the model that best predicted survival incorporated pre-treatment CTC number and LDH concentration alongside post-treatment changes in CTC number.