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. The first generation of prognostic factors included steroid receptors, along with DNA ploidy and S-phase fraction determined by flow cytometry. Currently, laboratory results have been obtained from more than 127,000 patients, and follow-up information is available on a subset of more than 25,000 of these patients. S-phase fraction was related to the ploidy status of the tumor. An increased incidence of aneuploidy and higher S-phase fractions were found in estrogen and progesterone receptor negative tumors, tumors from patients with positive axillary lymph nodes, tumors greater than 2 cm in diameter, and patients younger than 35 years of age. Preliminary survival analyses suggest that S-phase fraction and DNA ploidy, in combination with other prognostic factors, are powerful predictors of early disease relapse. The Oncology Research Network provides an important resource for examining the clinical significance of new laboratory assays and for expediting improvements in existing laboratory techniques.

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. Prognostic indexes were created that could be used to identify patients with very good or very poor prognoses.

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. We also need a method of evaluating these models' predictive ability instead of simply measuring goodness-of-fit, as is currently done. Often, prognostic factors are broken into two categories such as positive or negative. But this dichotomization may hide valuable prognostic information. We investigated whether continuous representations of factors, including standard transformations--logarithmic, square root, categorical, and smoothers--might more accurately estimate the underlying relationship between each factor and survival. We chose the logistic regression model, a special case of the commonly used Cox model, to test our hypothesis. The model containing continuous transformed factors fit the data more closely than the model containing the traditional dichotomized factors. In order to appropriately evaluate these models, we introduce three predictive validity statistics--the Calibration score, the Overall Calibration score, and the Brier score--designed to assess the model's accuracy and reliability. These standardized scores showed the transformed factors predicted three year survival accurately and reliably. The scores can also be used to assess models or compare across studies.

Prediction of relapse or survival in patients with node-negative breast cancer by DNA flow cytometry.

More accurate prediction of the prognosis in women with node-negative breast cancer may improve physicians' ability to identify the patients most likely to benefit from systematic adjuvant therapy. With this in mind, we performed DNA flow-cytometric measurements of ploidy and the fraction of cells in the synthesis phase of the cell cycle (S-phase fraction) on 395 specimens of node-negative breast cancer from our bank of frozen tumors, using the aliquots of pulverized frozen tissue from steroid-receptor assays. The median duration of follow-up in patients still alive at the time of analysis was 59 months. Thirty-two percent of the 345 specimens that could be evaluated were diploid, and 68 percent were aneuploid. The probability of disease-free survival at five years was 88 +/- 3 percent in patients with diploid tumors and 74 +/- 3 percent in those with aneuploid tumors (P = 0.02). The S-phase fraction was not a significant additional predictor of disease-free survival in patients with aneuploid tumors. However, the probability of disease-free survival in patients with diploid tumors and low S-phase fractions was 90 +/- 3 percent at five years, as compared with 70 +/- 13 percent in those with diploid tumors and high S-phase fractions (P = 0.007). Similar differences in overall survival were noted. We conclude that DNA flow-cytometric measurements of ploidy and S-phase fraction can be performed on frozen specimens of tumors and are potentially important predictors of disease-free and overall survival in patients with node-negative breast cancer.

Male fertility study on N,N-dimethylacetamide administered by the inhalation route to Sprague-Dawley rats.

Male Sprague-Dawley rats were exposed to N,N-dimethylacetamide (DMAC) and mated to untreated virgin females. Mean analytical exposure concentrations were 40, 116, and 386 ppm, respectively. A control group was exposed to air containing no DMAC. A total of 69 d of exposure to DMAC at these levels produced treatment-related effects of increased liver weights and liver/body weight ratios in the high- and medium-exposure groups of male rats. Reproductive data indicated no treatment-related effects on copulation efficiency or efficiency in effecting pregnancy, and there were no detectable treatment-related effects on preimplantation loss, postimplantation loss, embryotoxicity, or fetotoxicity in litters of females mated to males exposed to DMAC at the levels used in this study. The significance of these findings is discussed.

High-pressure liquid chromatographic studies of TCC and metabolites in experimental animals and man.

A reversed-phase high performance liquid chromatographic (HPLC) procedure for the analyses of 3,4,4'-trichlorocarbanilide (TCC) and its free and/or conjugated metabolic products in plasma/serum in described. A rapid, effective clean-up procedure, prior to chromatographic evaluation, involves a single-step combined protein removal and THF extraction. Detection of the TCC moiety after HPLC separation is by UV absorption at 265 nm; quantitation by peak height measurement. A detection limit of 10 ppb of TCC and/or metabolities has been demonstrated for this method. Verification of this method was by radiotracer counting of the appropriate HPLC peaks from plasma of animals adminstered 14C-TCC/TCC. The utility of this method was demonstrated in both animal pharmacological and toxicological studies and in human bathing studies.