The prognostic value of polymorphonuclear leukocyte elastase in patients with primary breast cancer

A variety of serine proteases, including urokinase-type plasminogen activator (uPA), plasmin,and polymorphonuclear leukocyte elastase (PMN-E), have been implicated in the processes of tumor cell invasion and metastasis. Besides degrading of matrix proteins, PMN-E has been shown to be able to cleave and inactivate plasminogen activator inhibitor-1 (PAI-1), the main inhibitor of uPA, and alpha2-antiplasmin, the natural inhibitor of plasmin, thus enabling an uncontrolled matrix degradation by the fibrinolytic enzymes. Because only limited data are available on a relationship between the tumor level of PMN-E and prognosis in primary breast cancer patients, in the present study we have measured with an ELISA the levels of PMN-E (in complex with alpha1-proteinase inhibitor) in cytosolic extracts of 1143 primary breast tumors. Levels of complexed PMN-E have been correlated with the lengths of metastasis-free survival (MFS), relapse-free survival, and overall survival, and a comparison was made with data previously obtained for uPA and PAI-1. Our results show that patients with a high PMN-E level in their primary tumor had a rapid relapse and an early death compared with patients with a low tumor level of PMN-E. This held true for node-negative and node-positive subgroups of patients as well. The relationship of PMN-E with a poor prognosis was especially obvious during short-term follow-up (0-60 months). In Cox multivariate regression analysis, corrected for the traditional prognostic factors, PMN-E was an independent prognostic factor, and high levels of PMN-E were associated with a poor MFS [hazard ratio (HR), 1.63; 95% confidence interval (CI), 1.23-2.16; P < 0.001], relapse-free survival (HR, 1.45; 95% CI, 1.10-1.89; P = 0.01), and overall survival (HR, 1.64; 95% CI, 1.20-2.23; P = 0.003). Furthermore, in all three multivariate models, PMN-E still added significantly to the model after the additional inclusion of the uPA. PMN-E was an independent prognostic factor for MFS even in the multivariate analysis including the traditional clinical prognostic factors and the strong established biochemical prognostic factors uPA and PAI-1. Our present study suggests that PMN-E is associated with breast cancer metastasis, and knowledge of the tumor PMN-E status might be helpful in selecting the appropriate individualized (adjuvant) treatment for patients with breast cancer

Contralateral breast cancer risk is influenced by the age at onset in BRCA1-associated breast cancer

BRCA1/2 mutation carriers diagnosed with breast cancer have a strongly elevated life-time risk of developing a contralateral tumour. We studied the contralateral breast cancer risk in 164 patients from 83 families with a proven BRCA1 mutation in relation to the age at diagnosis of the first primary breast cancer. In the actuarial outcomes after 10 years’ follow-up, 40% of the 124 BRCA1-patients diagnosed with breast cancer < 50 years had developed contralateral breast cancer, vs 12% of the 40 patients > 50 years at first diagnosis (Plogrank= 0.02). These data suggest that age at diagnosis of the first tumour should be taken into account when prophylactic mastectomy in BRCA1-patients is considered. © 2000 Cancer Research Campaig

Long-term strategies for flood risk management: scenario definition and strategic alternative design

This report reviews some mainstream existing methods of scenario development and use, as well as experiences with the design and assessment of strategic alternatives for flood risk management. Next, a procedure and methods are proposed and discussed. Thirdly, the procedure and methods are tried on the Schelde Estuary and the Thames Estuary and, finally, conclusions are draw

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

Background: Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples. Results: Here we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes. We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented. Conclusions: KC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Progressive neurological deficits in multiple myeloma: meningeal myelomatosis without MRI abnormalities

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