FLUORESCENCE-BASED ANALYSIS OF DNA-PLOIDY AND CELL-PROLIFERATION WITHIN FINE-NEEDLE SAMPLINGS OF BREAST-TUMORS - A NEW APPROACH USING AUTOMATED IMAGE CYTOMETRY

BACKGROUND. Automated image cytometry can allow concurrent quantificat ion of several parameters in each individual cell within a population, opening new possibilities for diagnosis and prognosis. In this study, the authors investigated the capacity of this method for performing a bivariate analysis of DNA ploidy and synthesis in fine-needle samplin gs obtained without aspiration from breast tumors. METHODS. Samplings from 25 unselected cases of ductal infiltrative breast adenocarcinoma and 2 cases of fibroadenoma were analyzed. For each case, 3-5 slides ( containing approximately 1000 cells each) were quantified to assess ex perimental precision. Ploidy was determined by fluorescent staining of DNA using 4,6-diamidino-2-phenylindole (DAPI). Contaminating lymphocy tes were taken as internal controls to calculate DNA indices. DNA synt hesis was analyzed by immunofluorescent detection of 5-bromodeoxyuridi ne (BrdU) incorporation. Measurements were compared with flow cytometr ic data obtained from the same patients. RESULTS. Relative error in de termination of DNA indices was generally below 5%. Determination of pr oliferation indices were more variable, with a mean relative error of 25%. Two different populations of BrdU positive cells were detected sy stematically, one in the diploid and another in the aneuploid fraction . For both cytometric methods, DNA indices were similar in all 27 case s, whereas BrdU labeling indices showed no significant correlation in 13 cases. The remaining cases were not comparable due to lack of flow cytometric data. Labeling indices obtained by image cytometry did not reveal any significant correlation with Scarff-Bloom-Richardson gradin g or clinical staging. CONCLUSIONS. Automated image cytometry allows c oncurrent measurement of ploidy and cell proliferation within individu al breast carcinoma cells. Statistical reliability can be reached with a relative small number of cells (1000), which is crucial for samples in which the cell number is too low for flow cytometry analysis. Visu al control for artifact elimination and better characterization of cel l populations makes this a powerful tool for tumor cell investigation. Automated image cytometry allows the obtainment of valuable prognosti c parameters of traditional flow cytometry with the relatively small n umber of cells obtained in aspiration procedures. Cancer (Cancer Cytop athol) 1998;84:309-16, (C) 1998 American Cancer Society.