4.5 Article

Circulating Tumor Cells in Patients with Castration-Resistant Prostate Cancer Baseline Values and Correlation with Prognostic Factors

Journal

CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
Volume 18, Issue 6, Pages 1904-1913

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1055-9965.EPI-08-1173

Keywords

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Funding

  1. Department of Energy [DE-FG02-06ER64280]
  2. Veridex, Inc

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Purpose: Circulating tumor cells (CTC) have been recently accepted by the Food and Drug Administration of the United States as a prognostic tool in advanced prostate cancer. However, a number of questions remain about the use of the test. The optimal clinical cut-off has never been determined. Also, the predictive value of CTCs in the setting of low-burden advanced prostate cancer has not been evaluated. Herein we describe our experience with the CellSearch method of CTC enumeration. Experimental Design: CTCs enumerated from 100 patients with castration-resistant prostate cancer were correlated with clinicopathologic characteristics and conventional biomarkers, such as prostate-specific antigen and lactate dehydrogenase. Patients received ongoing medical oncologic follow-up for up to 26 months, and overall survival status was documented. Results: Forty-nine of the patients (49%) were alive at the end of the study. CTC counts correlate well with overall survival (P < 0.001) but are also tightly interrelated to other biomarkers. Threshold analysis identified 4 CTC/7.5 cc (compared with the approved value of 5) as an optimal cut-off value with respect to correlation with survival outcomes as well as predictive of metastatic disease. Univariate analysis confirmed a tight interrelationship between cut-off CTC values and biomarkers. Multivariate analysis with bootstrap sampling validation identified lactate dehydrogenase (P = 0.002) and CTCs (P = 0.001) as independently prognostically significant. Conclusions: Baseline CTC values provide important prognostic information and specific prediction of metastatic disease. Their presence correlates with classic biomarkers. (Cancer Epidemiol Biomarkers Prev 2009;18(6):1904-13)

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