Evaluation of a modeling system for S-phase estimation in breast cancer by flow cytometry.

Using software programs provided by Coulter Electronics, we have developed an analysis system that would address problems encountered in DNA flow cytometric analysis of heterogeneous solid tumor populations, especially where the G2-M phase of the diploid population contaminates the S-phase of the aneuploid population, causing an overestimation of cells in S phase. We used the PARA 1 and PARA 2 programs in concert and developed three analysis models: (a) for euploid tumors; (b) for hyperdiploid tumors with overlapping populations; and (c) for near-diploid aneuploid tumors. Our purpose in this paper is to determine the limits and reproducibility of this analysis system with an emphasis on tumors with overlapping populations. Aliquots of frozen, pulverized breast tumor tissue (50 to 100 mg), routinely used in the steroid receptor assay, were used for routine flow cytometric measurement of the DNA index and S-phase fraction. To determine the accuracy of the analysis when overlapping populations were present, we mixed an aneuploid breast cancer cell line with human blood lymphocytes in varying ratios. A 10% mixture of aneuploid cells, the lowest mixture tested, still allowed analysis results within 95% confidence limits. Reproducibility of the system was assessed on frozen breast tumor tissue by intra- and interassay variation studies measuring cell cycle parameters and coefficient of variation of the G0-G1 peak width. Within any sample the amount of variation (+/- 2 SD) for the G0-G1 value was +/- 4.40 for intraassay and +/- 4.60 for interassay, and the amount of variation for S phase was +/- 3.0 and +/- 3.2 for intraassay and interassay, respectively. There was no difference in the variation of estimates for G2-M (+/- 2.6 for both intra- and interassay). In this study, the coefficient of variation of the G0-G1 peak greater than 5% was defined as unacceptable for accurate analysis, with the conclusion that S-phase fractions in aneuploid tumors can be routinely analyzed in human breast tumor biopsies despite tumor cell heterogeneity.

Prognostic significance of S-phase fraction in good-risk, node-negative breast cancer patients.

PURPOSE: Formalin-fixed, paraffin-embedded tissues from axillary node-negative breast cancer patients were analyzed by flow cytometry to determine the prognostic significance of DNA ploidy and S-phase fraction (SPF). PATIENTS AND METHODS: All patients were registered on a good-risk control arm of an intergroup clinical trial. They had small- to intermediate-sized (less than 3 cm), estrogen receptor (ER)-positive tumors and received no adjuvant therapy after modified radical mastectomy or total mastectomy with low axillary-node sampling. The median follow-up was 4.8 years. RESULTS: Assessable ploidy results were obtained from 92% of the 298 specimens studied (51% diploid, 49% aneuploid), and SPFs were assessable for 83% of the tumors. SPFs for diploid tumors ranged from 0.7% to 11.9% (median, 3.6%), compared with a range of 1.2% to 26.7% (median, 7.6%) for aneuploid tumors (P less than .0001). No significant differences in disease-free or overall survival were observed between patients with diploid and aneuploid tumors. Using different SPF cutoffs by ploidy status (4.4% for diploid, 7.0% for aneuploid), patients with low SPFs had significantly longer disease-free survival rates than patients with high SPFs (P = .0008). The actuarial 5-year relapse rates were 15% and 32% for patients with low (n = 142) and high SPFs (n = 105), respectively. Similar relationships between SPF and clinical outcome were observed for patients with diploid tumors (P = .053) and for patients with aneuploid tumors (P = .0012). CONCLUSION: S-phase fraction provides additional prognostic information for predicting disease-free survival for axillary node-negative breast cancer patients with small, ER-positive tumors.

Validation and quality control of immunophenotyping in clinical flow cytometry.

Clinical flow cytometry has evolved from two-parameter quantitative assessment of peripheral blood lymphocytes to six-parameter qualitative evaluation of bone marrow for hematopathology. Leukemia and lymphoma immunophenotyping represent an extremely important complement to morphology in the diagnosis and monitoring of hematopoietic malignancies. The complexity of five- and six-parameter analyses and the interpretation of the data rely on standardization and validation of the instrument, the reagents and the procedure. In addition, flow cytometry laboratories in the U.S. are required to document proficiency testing, sample preparation, method accuracy, specificity, sensitivity and precision. NCCLS and the U.S.-Canadian Consensus Conference have provided recommendations, but each laboratory is ultimately responsible for validating its own qualitative and quantitative procedures. This paper reviews procedures for validation and quality control of all aspects of the operation of a clinical flow cytometry service.

Differential influence of dexamethasone on the activity and synthesis of beta-galactoside specific lectin (galaptin) during postnatal lung development.

This study examined the regulation of the activity and synthesis of lung beta-galactoside specific lectin (galaptin) by dexamethasone (Dex). The effect of Dex was different depending on the postnatal period administered. Dex decreased galaptin activity and synthesis when administered daily during the "critical period" of alveolarization (postnatal days 3-13) described by Massaro and coworkers (J Clin Invest 76:1297-1305, 1985). The normal rise in galaptin activity (both soluble and membrane- or particulate-bound) observed for untreated controls was prevented by Dex (0.22 mg/pup day-1) treatment. Short-term (2 days) administration on days 4 and 5 had little effect. However, short-term administration of Dex at a later time (days 10-11) increased galaptin activity and synthesis. Both soluble and particulate-bound galaptin were similarly increased by Dex. These studies indicate that both pools of galaptin may be synthesized and activated by a similar mechanism. The effects of Dex on galaptin expression by the postnatal rat lung suggests that endogenous glucocorticoids play a role in the regulation of galaptin activity. The findings for rats given this level of Dex suggest that the period in which exogenous glucocorticoids are administered initially, either as short-term or chronic treatments, is important for the direction of their effect on galaptin expression.

A photothermal interferometer for gas-phase ammonia detection.

Detection of gas-phase ammonia is particularly challenging because ambient ammonia concentrations may be less than 1 ppb (molecules of NH(3) per 10(9) molecules of air), ammonia sticks to many materials commonly used to sample air, and particles containing ammonium may interfere with gas-phase measurements. We have built a new and sensitive photothermal interferometer to detect gas-phase ammonia in situ, under typical atmospheric conditions. Ammonia molecules in sampled air absorb infrared radiation from a CO(2) laser at 9.22 µm, with consequent collisional heating, expansion, and refractive index change. This change in refractive index is detected as a phase shift in one arm of a homodyne interferometer. Measurements of vibrational and electrical noise in the interferometer correlate to an instrumental lower limit of detection of 6.6 ppt ammonia in 1 s. The CO(2) laser output is modulated at 1.2 kHz, and the ac signal from the interferometer is measured with a lock-in amplifier. The detector is zeroed by sampling through a H(3)PO(4)-coated denuder tube and is calibrated by dynamic dilution of two permeation tube outputs and by standard addition. Signal gain is insensitive to CO(2) or H(2)O in the sample, and the signal is linear over 5 orders of magnitude. The instrument 2σ precision is 31 ppt when the signal is integrated for 100 s and 250 ppt with a 1-s integration time. The windowless sample cell and inlet is fabricated entirely of glass to minimize sample loss and hysteresis. The instrument response time is demonstrated to be about 1 s.

Role of steroid hormone receptors as prognostic factors in primary breast cancer.

A review and update of published studies on estrogen receptor (ER) and progesterone receptor (PgR) as prognostic factors in breast cancer support the following conclusions. In stage I breast cancer, the lack of ER seems to be the most important factor for predicting earlier recurrence and poorer survival. In stage II breast cancer, PgR content appears to be better than ER content in predicting disease-free survival and PgR content is as important as ER content in predicting overall survival. The benefits of adjuvant endocrine therapy are better predicted by the presence or absence of PgR than by the presence or absence of ER. Measurement of proliferative activity (S-phase DNA) by thymidine labeling or flow cytometry and of aneuploidy by flow cytometry also provides prognostic information. The strong correlations between tumor receptor content, percent S-phase cells, and aneuploidy suggest that these measurements in concert might identify a subset of stage I breast cancer patients at increased risk for recurrence, who would thus be potential candidates for adjuvant therapy.

DNA flow cytometry and prognostic factors in 1331 frozen breast cancer specimens.

Breast cancer proliferative capacity as determined by the DNA thymidine labeling index, along with estrogen and progesterone receptor status, is highly predictive for risk of relapse and overall survival. Recently, DNA ploidy and proliferative capacity (S-phase fraction [SPF]) as determined by flow cytometry have also shown significant prognostic value. The authors have developed a technique which allows a 50 to 100 mg aliquot of the same frozen breast tumor specimen routinely employed in steroid receptor assays, to be assayed for both DNA ploidy and SPF by flow cytometry. Of the 1331 tumors examined, DNA histograms were evaluable for ploidy in 89% (1184) of specimens examined; 57% of these were aneuploid. Adapting a trapezoidal model to estimate SPF in both diploid and aneuploid tumors, the authors found 81% (1084) to be evaluable for SPF, with a median SPF of 5.8% for the entire population. The median SPF was significantly lower in diploid tumors (2.6%) than in aneuploid tumors (10.3%, P less than 0.0001). Both aneuploidy and high SPF were strongly associated with absence of steroid receptors. Aneuploid tumors showed more striking differences in the frequency of high S-phase values with respect to receptor status and age or menopausal status, whereas diploid but not aneuploid tumors showed lower SPF in node-negative versus node-positive patients. Because it is particularly important to identify the high-risk minority of node-negative patients, the authors examined the node-negative group separately. High SPF subgroups appeared in each category of receptor status and age or menopausal status within the node-negative group, suggesting that SPF will be an independent prognostic factor. With the DNA flow cytometric methods used here, it is now practical to determine ploidy and SPF for nearly every breast cancer patient. These factors, which show associations with established prognostic factors, such as receptor status can now be fully evaluated for their prognostic significance in broad patient populations.

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.

Neural Network Analysis of DNA flow cytometry histograms.

A pattern recognition system based on Neural Network Analysis, a form of artificial intelligence, was used to search DNA flow cytometry histograms for features that correlated with breast cancer patients' risk of relapse. DNA flow cytometry histograms and clinical follow-up information from 796 breast cancer patients were used to train a Neural Network to predict the clinical outcome of patients in a separate independent set of 794 patients. Median follow-up in this patient data base was short, 23 months. Neural Network Analysis resulted in a model that evaluated DNA flow cytometry histograms differently than conventional analysis, which categorizes the histograms by ploidy and S-phase fraction. Neural Network Analysis appeared to identify low risk and high risk subsets of patients as accurately as conventional analysis. Neural Network Analysis placed heavy emphasis on the region to the right of the diploid G2/M peak, where a subpopulation of nuclei with high DNA content is seen even in many histograms scored as diploid by conventional techniques. The number of nuclei in this region was found to be a powerful predictor of patient outcome, and multivariate analysis showed that the number of nuclei in this region and the S-phase fraction both were independently predictive of relapse. This pilot study suggests that conventional analysis (based on a mechanistic interpretation of regions in flow cytometry histograms) might be used in conjunction with and improved by pattern recognition systems or insights derived from them.

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.

A demonstration that breast cancer recurrence can be predicted by neural network analysis.

Neural Network Analysis, a form of artificial intelligence, was successfully used to predict the clinical outcome of node-positive breast cancer patients. A Neural Network was trained to predict clinical outcome using prognostic information from 1008 patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as length of clinical followup, relapse status, and time of relapse. The ability of the trained Network to determine relapse probability was then validated in a separate set of 960 patients. The Neural Network was as powerful as Cox Regression Modeling in identifying breast cancer patients at high and low risk for relapse.

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.