DNA ploidy is a valuable predictor for prognosis of patients with resected renal cell carcinoma.

Cancer

Department of Internal Medicine, Hepatology, and Gastroenterology, Charité, Campus Virchow-Klinikum, Humboldt-University, augustenberger Platz 1, 13353 Berlin, Germany.

Published: November 2001

Background: Renal cell carcinomas (RCCs) are heterogeneous and include several distinct entities with a range of biologic and clinical behaviors from relatively favorable to extremely aggressive. The heterogeneity leads to unpredictable outcome and survival. DNA ploidy is a relatively new predictor differentiating diploid from aneuploid tumor cells according to regular or irregular DNA content. The authors evaluated the predictive value of DNA ploidy in patients who underwent resection because of RCC.

Methods: In a prospective study, 180 patients who underwent resection because of RCC were investigated. DNA cytometry was conducted on each resected tumor to determine DNA ploidy. Patients were completely followed up until death or up to 12 years.

Results: Survival analysis showed that patients who underwent resection because of RCC in tumor classifications pT1, pT2, and pT3 survived 10 years in 85%, 53%, and 8% of cases, respectively. Patients suffering from small tumors (pT1 and pT2, n = 44) with diploid nuclei survived 10 years in 94% but only in 8% if the tumor was aneuploid (n = 55). In addition, 91% of patients who underwent resection of large tumors (pT3, n = 12) with diploid nuclei survived 10 years, but no patient with large and aneuploid tumor (n = 51) survived more than 3 years. Furthermore, 92% of all patients afflicted from diploid RCC survived 10 years. This finding was independent of tumor stage.

Conclusions: The results of this study suggest that DNA ploidy is a significant and independent predictor for survival of patients afflicted from RCC and superior to tumor classification and grade. DNA ploidy is a reliable prognostic factor for RCC and yields considerable information for patient management and predicting clinical outcome.

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