Lack of prognostic value of cathepsin D levels for predicting short term outcomes of breast cancer patients.

The value of cathepsin D determinations done on tumor cytosols in evaluating the prognosis of breast cancer patients has been debated in the literature. Our previous work suggested that cathepsin D determinations were not of prognostic value, but in that study we used immunoblotting and immunohistochemical methods rather than the more widely used double antibody immunoradiometric (IRMA) assay for measuring cathepsin levels. Here we report our results determining cathepsin D using components of a commercially available IRMA system on a large patient sample (n = 1984).

DNA ploidy, S-phase, and steroid receptors in more than 127,000 breast cancer patients.

Several potential prognostic factors are available today for patients with breast cancer, and many more are being identified and studied. To evaluate the clinical utility of these factors, it will be necessary to measure them on a large number of patients, and then follow these patients so that multivariate survival analyses can be performed. The Oncology Research Network was established in 1986 by the University of Texas Health Science Center at San Antonio and Nichols Institute Reference Laboratories in order to evaluate the clinical utility of new prognostic factors for patients with primary breast cancer.

How to integrate steroid hormone receptor, flow cytometric, and other prognostic information in regard to primary breast cancer.

A large group of patients with node-positive breast cancer was divided into a training set (n = 851) and a validation set (n = 432) to demonstrate techniques for integrating steroid hormone receptor status, DNA flow cytometric findings, and other prognostic factors to predict patient survival. Multivariate analyses showed that estrogen receptor status, the number of involved axillary lymph nodes, patient age, S-phase fraction, progesterone receptor status, and tumor size were significant predictors of survival in patients with node-positive breast cancer. Techniques for optimizing and validating a cut point for a new prognostic factor and for examining alternative representations of prognostic factors were demonstrated.

Making the most of your prognostic factors: presenting a more accurate survival model for breast cancer patients.

Determining an appropriate level of adjuvant therapy is one of the most difficult facets of treating breast cancer patients. Although the myriad of prognostic factors aid in this decision, often they give conflicting reports of a patient's prognosis. What we need is a survival model which can properly utilize the information contained in these factors and give an accurate, reliable account of the patient's probability of recurrence.