Circulating tumor cells count predicts survival in colorectal cancer patients

J Gastrointestin Liver Dis. 2014 Sep;23(3):279-84. doi: 10.15403/jgld.2014.1121.233.arom1.

Abstract

Background and aims: Data on the potential of circulating tumor cells (CTC) count in predicting overall survival (OS) in patients with colorectal cancer are timely and worthy of interest. This study aimed to evaluate the prognostic role of CTC count in both localized and metastatic colorectal cancer patients.

Methods: Consecutive patients with histological diagnosis of colorectal cancer were enrolled. CTC count was performed, by using a quantitative immunofluorescence method, at baseline (T0) and 1 month following start of chemotherapy (T1). A CTC count <2 was considered negative, whilst a CTC level >/= 2 was positive. Overall survival was calculated accordingly.

Results: A total of 75 colorectal cancer patients were enrolled, including 54 stages I-III and 21 stage IV patients. Overall, 21 (28%) patients had a positive CTC count at baseline, and it was significantly associated with a worse prognosis as compared to a negative status (OS: 36.2 vs. 61.6 months; P = 0.002). CTC count remained positive after chemotherapy in 22.4% of the patients and it was an independent prognostic factor of OS (P = 0.03; Hazard Ratio: 3.55; 95% CI: 1.1-11.5).

Conclusions: This study found that the presence of CTCs is associated with a reduced survival in colorectal cancer patients. Further studies aimed at testing such a predictive value in early stage colorectal cancer are awaited.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cell Count
  • Colorectal Neoplasms / blood
  • Colorectal Neoplasms / mortality
  • Colorectal Neoplasms / pathology*
  • Colorectal Neoplasms / therapy
  • Disease-Free Survival
  • Female
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Neoplasm Metastasis
  • Neoplasm Staging
  • Neoplastic Cells, Circulating / pathology*
  • Predictive Value of Tests
  • Proportional Hazards Models
  • Risk Factors
  • Time Factors
  • Treatment Outcome