S-phase fraction as a predictor of prognosis in juvenile respiratory papillomatosis.

OBJECTIVE To determine whether DNA ploidy and the S-phase fraction are predictive of the clinical course in children with recurrent respiratory papillomatosis. DESIGN Masked compression of DNA analysis findings to the clinical course of the disease. SETTING Tertiary referral center. PATIENTS All pediatric patients treated for recurrent respiratory papillomatosis at our institution between 1989 and 1995 who had adequate follow-up and whose primary biopsy specimen was available for examination. Fifty-five patients met these criteria. METHODS Information was collected from the case notes on the patient's age at presentation, sex, sites of disease, duration of active disease, and frequency of operative interventions. Flow cytometric analysis was performed on the archival paraffin-embedded primary biopsy specimen obtained at the initial surgical excision, providing DNA content and percentages of S-phase cells. The investigators who performed the DNA analysis were masked to the clinical course. RESULTS The age of the patients at presentation ranged from 3 months to 16 years. Thirty patients had involvement in more than 1 anatomical site. The disease in 10 patients had spread to the distal tracheobronchial tree. The patients underwent a total of 1124 procedures, with a frequency range of 7 to 27 per year. All cell populations studied were diploid. The percentage of S-phase cells was significantly higher in the primary biopsy specimen from patients with disease characterized by more frequent recurrences, multiple sites, and distal extension (P<.05). In multiple regression analysis, the S-phase fraction was found to be an independent and powerful prognostic factor for aggressive disease. CONCLUSIONS The S-phase fraction may be predictive of the clinical course in patients with juvenile respiratory papillomatosis. Prospective studies are needed to assess the diagnostic and clinical value of our primary results and to determine whether DNA analysis can assist in identifying patients at increased risk for an aggressive clinical course.