Targeting the urokinase plasminogen activator receptor with a monoclonal antibody impairs the growth of human colorectal cancer in the liver.

Autor: Van Buren G 2nd; Department of Surgical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA., Gray MJ, Dallas NA, Xia L, Lim SJ, Fan F, Mazar AP, Ellis LM
Jazyk: angličtina
Zdroj: Cancer [Cancer] 2009 Jul 15; Vol. 115 (14), pp. 3360-8.
DOI: 10.1002/cncr.24371
Abstrakt: Background: Urokinase plasminogen activator receptor (uPAR) expression has been shown to correlate with poor prognosis in colorectal cancer (CRC). The authors hypothesized that targeting uPAR, a receptor involved in cell proliferation, migration, invasion, adhesion, and angiogenesis, would impair the growth of CRC in the liver, the most common site of metastasis.
Methods: Human CRC cell lines were examined for uPAR expression by Western blot analysis. The in vitro effects of the uPAR monoclonal antibody (MoAb) (ATN-658) were tested in proliferation and migration assays. For in vivo studies, human HCT116 CRC cells were injected directly into the livers of mice in 2 separate studies, the first to determine the effect of therapy with ATN-658 on small-volume disease (therapy begun on Day 4), and a second study to determine the effect of therapy on established disease (therapy begun on Day 12). Mice were randomized to receive either nonspecific immunoglobulin G MoAb (control) or ATN-658, and were sacrificed 1 month after tumor implantation.
Results: uPAR was expressed by all CRC cell lines studied. In vitro, ATN-658 had minimal effect on CRC proliferation in monolayers, but significantly decreased CRC cell migration. In vivo, ATN-658 lead to significant reductions in tumor growth versus control when initiated either 4 or 12 days after tumor implantation (-65% vs control [P < or = .05] and -85% vs control [P < or = .05]). ATN-658 significantly inhibited in vivo tumor cell proliferation in both studies.
Conclusions: uPAR MoAb therapy impaired CRC tumor growth in the liver in both small-volume and large-volume disease models.
Databáze: MEDLINE
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