Article

Tissue-type plasminogen activator (tPA) in breast cancer: relationship with clinicopathological parameters and prognostic significance.

Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain.
Breast Cancer Research and Treatment (Impact Factor: 4.2). 04/2005; 90(1):33-40. DOI: 10.1007/s10549-004-2624-x
Source: PubMed

ABSTRACT Tissue-type plasminogen activator (tPA) is a serine protease primarily involved in the intravascular dissolution of blood clots. High intratumoral tPA levels are associated with prognosis in several human tumors. In addition, tPA has been shown to be an estrogen-inducible protein in human breast cancer cell lines. The aim of the present study was to analyze the cytosolic tPA content in primary breast carcinomas and its potential clinical value.
tPA was measured by a solid-phase enzyme immunoassay in tumor cytosol samples obtained from 800 patients with breast cancer. The median follow-up period was of 49.2 months.
Cytosolic tPA levels ranged widely in breast carcinomas (median: 3.9; range: 0.1- 315.3 ng/mg protein). tPA levels were significantly lower in large tumors, as well as in those showing poor differentiation, estrogen (ER) or PgR-negativity, aneuploidy, or a high S-phase fraction. In addition, low tPA intratumoral levels were associated with a high probability of both shortened relapse-free and overall survival in all patients and in the subgroup with node-negative tumors. However, our results did not show any significant relationship between intratumoral tPA levels and prognosis in the different subgroups of patients, stratified according to the type of systemic adjuvant therapy received (chemotherapy, tamoxifen or chemotherapy plus sequential tamoxifen).
The results of the present investigation indicate that low intratumoral tPA levels are associated with aggressiveness and poor prognosis in breast cancer patients. However, the study suggests that tPA levels do not predict response to systemic adjuvant therapy.

0 Bookmarks
 · 
79 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: For decades tumors have been recognized as "wounds that do not heal." Besides the commonalities that tumors and wounded tissues share, the process of wound healing also portrays similar characteristics with chronic fibrosis. In this review, we suggest a tight interrelationship, which is governed as a concurrence of cellular and microenvironmental reactivity among wound healing, chronic fibrosis and cancer development/progression (i.e., the WHFC triad). It is clear that the same cell types, as well as soluble and matrix elements that drive wound healing (including regeneration) via distinct signaling pathways, also fuel chronic fibrosis and tumor progression. Hence, here we review the relationship between fibrosis and cancer through the lens of wound healing.
    Physiological Genomics 02/2014; · 2.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The PDGF (platelet-derived growth factor) family members are potent mitogens for cells of mesenchymal origin and serve as important regulators of cell migration, survival, apoptosis and transformation. Tumour-derived PDGF ligands are thought to function in both autocrine and paracrine manners, activating receptors on tumour and surrounding stromal cells. PDGF-C and -D are secreted as latent dimers, unlike PDGF-A and -B. Cleavage of the CUB domain from the PDGF-C and -D dimers is required for their biological activity. At present, little is known about the proteolytic processing of PDGF-C, the rate-limiting step in the regulation of PDGF-C activity. In the present study we show that the breast carcinoma cell line MCF7, engineered to overexpress PDGF-C, produces proteases capable of cleaving PDGF-C to its active form. Increased PDGF-C expression enhances cell proliferation, anchorage-independent cell growth and tumour cell motility by autocrine signalling. In addition, MCF7-produced PDGF-C induces fibroblast cell migration in a paracrine manner. Interestingly, PDGF-C enhances tumour cell invasion in the presence of fibroblasts, suggesting a role for tumour-derived PDGF-C in tumour-stromal interactions. In the present study, we identify tPA (tissue plasminogen activator) and matriptase as major proteases for processing of PDGF-C in MCF7 cells. In in vitro studies, we also show that uPA (urokinase-type plasminogen activator) is able to process PDGF-C. Furthermore, by site-directed mutagenesis, we identify the cleavage site for these proteases in PDGF-C. Lastly, we provide evidence suggesting a two-step proteolytic processing of PDGF-C involving creation of a hemidimer, followed by GFD-D (growth factor domain dimer) generation.
    Biochemical Journal 02/2012; 441(3):909-18. · 4.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Glucocorticoid receptor (GR) activation can inhibit breast epithelial and cancer cells from undergoing programmed cell death in response to diverse apoptotic stimuli. Understanding the mechanisms underlying inappropriate cell survival mechanisms is important for treating breast cancer because if we can reverse these mechanisms, therapies designed to kill tumor cells are likely to be more effective. Recently, genome-wide DNA microarrays have provided a glimpse into the signals and interactions within regulatory pathways of the cell. These arrays enable simultaneous measurement of mRNA abundance of most, if not all, identified genes in a genome under different physiological conditions. Currently, two types of microarray experiments are frequently performed in laboratories. The first is a single time point microarray experiment, and the second is a time course microarray experiment. Single time point microarray experiments are effective in identifying genes regulated by a given treatment, e.g., direct target genes of a hormone treatment. However, because molecular pathways are dynamic processes that take place over time, single time point microarray experiments may not allow us to identify dynamic molecular pathways. This problem can be approached by performing a time course microarray experiment, which measures gene expression changes at various time points following a given treatment. In this chapter, we first describe how to identify target genes of a given treatment using a single time point microarray data analysis. We then present three alternate bioinformatics approaches to uncover molecular mechanisms from time course microarray data. Finally, we present a novel bioinformatics approach for analyzing time course microarray data in order to identify novel GR-mediated breast cancer cell survival pathways.
    12/2007: pages 165-183;