DNA flow cytometric analysis and prognosis of axillary lymph node-negative breast carcinoma

Abstract

Methods: The prognostic significance of flow cytometric analysis in patients with node-negative invasive breast carcinoma was evaluated in a retrospective series of 158 patients with a minimum follow-up study of 9 years.

Results: The ploidy status could be assessed in 147 specimens (93%), and the proliferative phase or S-phase fraction (SPF) could be assessed in 136 tumors (86%); 70 tumors (48%) were diploid, 49 tumors (33%) were aneuploid, and 28 tumors (19%) were tetraploid. Ploidy status and SPF were correlated significantly with tumor size, histologic grade, nuclear grade, and mitotic rate. By itself, ploidy was not a statistically significant prognostic factor, although all of the patients with multiploid and hypertetraploid tumors had recurrence of disease. The SPF was related significantly to recurrence of disease (P = 0.04). However, when multivariate analysis of various histopathologic variables was performed, SPF ceased to be a significant prognostic determinant, whereas peritumoral lymphovascular invasion was the most important variable. The combination of tumor size and flow cytometric parameters permitted stratification into three groups with different prognoses at the 9-year follow-up review (P less than 0.001). In the low-risk group (diploid tumors less than or equal to 2 cm in diameter with a low SPF or small tetraploid tumors), the recurrence rate was 12%. In the intermediate-risk group (diploid tumors greater than 2 cm in diameter with a low SPF or aneuploid tumors with a low SPF), the recurrence rate was 21%. In the high-risk group (diploid or aneuploid tumors with a high SPF or large tetraploid tumors), the recurrence rate was 49%. The high-risk group status remained a significant variable in the Cox proportional hazards multivariate analysis model.

Conclusions: These results indicate that flow cytometry in breast carcinoma contributes useful but limited prognostic information and stress the importance of using multiple prognostic factors to improve prognostication and optimize patient management.