Pooled analysis of prognostic impact of uPA and PAI-1 in breast cancer patients

• Published in: Thrombosis and haemostasis Vol. 90; no. 3; p. 538
• Main Authors: Look, Maxime, van Putten, Wim, Duffy, Michael, Harbeck, Nadia, Christensen, Ib Jarle, Thomssen, Christoph, Kates, Ronald, Spyratos, Frédérique, Fernö, Mårten, Eppenberger-Castori, Serenella, Fred Sweep, C G J, Ulm, Kurt, Peyrat, Jean-Philippe, Martin, Pierre-Marie, Magdelenat, Henri, Brünner, Nils, Duggan, Catherine, Lisboa, Björn W, Bendahl, Pär-Ola, Quillien, Véronique, Daver, Alain, Ricolleau, Gabriel, Meijer-van Gelder, Marion, Manders, Peggy, Edward Fiets, W, Blankenstein, Marinus, Broët, Philippe, Romain, Sylvie, Daxenbichler, Günther, Windbichler, Gudrun, Cufer, Tanja, Borstnar, Simona, Kueng, Willy, Beex, Louk, Klijn, Jan, O'Higgins, Nial, Eppenberger, Urs, Jänicke, Fritz, Schmitt, Manfred, Foekens, John, Bendah, Pär-Ola
• Format: Journal Article
• Language: English
• Published: Germany 01-09-2003
• Subjects: Breast Neoplasms - diagnosis; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Disease-Free Survival; Female; Humans; Lymph Nodes - pathology; Multivariate Analysis; Neoplasm Metastasis; Plasminogen Activator Inhibitor 1 - analysis; Predictive Value of Tests; Prognosis; Risk Assessment; Sensitivity and Specificity; Survival Analysis; Urokinase-Type Plasminogen Activator - analysis
• ISSN: 0340-6245
• DOI: 10.1160/th02-11-0264

In this report we present an extension of the pooled analysis of the prognostic impact of urokinase-type plasminogen activator (uPA) and its inhibitor PAI-I in breast cancer patients. We analyzed a different endpoint, metastasis-free survival (MFS). We checked the consistency of the estimates for uPA and PAI-1 for relapse-free survival (RFS) and MFS exploring possible sources of heterogeneity. Nodal status, the most important prognostic factor for breast cancer, introduced heterogeneity in the uPA/PAI-1 survival analyses, reflecting the interaction between nodal status and uPA/PAI-1. The estimates for uPA and PAI-1 were found to be consistent, even when a different transformation of their values was used. The heterogeneity of the separate data sets decreased if the levels of uPA and PAI-1 were ranked, data sets were pooled, and the analyses corrected for the base model that included all traditional prognostic factors, and stratified by data set. We conclude that uPA and PAI-1 are ready to be used in the clinic to help classify breast cancer patients into high and low risk groups.