PREDICTION OF METASTATIC POTENTIAL BY CANCER CELL MOTILITY IN THE DUNNING R-3327 PROSTATIC ADENOCARCINOMA INVIVO MODEL

Many grading systems for prostatic carcinoma exist; however, none allows pathologists to predict accurately the prognosis of individual patients. The Dunning R-3327 prostatic adenocarcinoma model consist of sublines of known metastatic potential which were indistinguishable until recently. A visual grading system of cancer cell motility distinguished the metastatic potentials of Dunning sublines maintained in vitro and was validated prospectively. We used this grading system to assess the metastatic potential of cells harvested directly from in vivo Dunning tumors. We graded the motility of cells from three Dunning sublines of low (< 10%) (G, AT1, AT2) and three sublines of high (> 90%) AT3, MAT-LyLu, PAT2) metastatic potential. Cells obtained from primary tumors were studied by time lapse videomicroscopy after passages 1, 3 and 5 in vivo and in vitro. Membrane ruffling, pseudopodal extension and cellular translation were graded 0-10. Serial analysis of mean and heterogeneity (coefficient of variation) of membrane ruffling, pseudopodal extension and cellular translation demonstrated that subline motility grades were assessed adequately by a sample of 10 cells. Motility did not depend upon whether cells were maintained in vitro or in vivo; however, motility increased with successive passages in four of six sublines. In 60 cells harvested directly from the fifth in vivo passage, three sublines of low metastatic potential were distinguished from three sublines of high metastatic potential (Student's t test, p < 0.01). Individual cells from the sublines were identified correctly as high or low metastatic in 83, 78 and 70% of cases by membrane ruffling, pseudopodal extension and cellular translation respectively, and logistic regression analysis failed to improve classification accuracy. A visual grading system of cancer cell motility described the metastatic potential of in vivo neoplasms in the Dunning model and may warrant testing in human prostatic cancer.