PDTM-24. PILOT STUDY OF CIRCULATING TUMOR CELLS IN PEDIATRIC HIGH GRADE BRAIN TUMORS. (5th November 2018) - Batth, Izhar; Zaky, Wafik; Chen, Ssu-Hsien; Huang, Wilber; Albert, Aish; Daw, Najat; Gill, Jonathan; Khatua, Soumen; Li, Shulin
Abstract: BACKGROUND: Brain tumors are the second most common malignancy in children, and are the leading cause of death from childhood cancer. Although significant strides have been achieved in improving survival of most childhood malignancies, the outcome for children with brain tumors has remained poor. Advanced imaging techniques can reduce diagnostic uncertainty, but the need to identify better disease markers is immense and remains unmet. In that regard, analysis of circulating biomarkers is a novel and promising avenue for monitoring disease status. Despite increasing use in tumor research, circulating tumor cells (CTCs) have not been studied in pediatric brain tumor and only scarcely investigated in adult brain tumors. METHOD: Based on previous research showing cell surface vimentin (CSV) as a key surface marker for CTC detection, we developed an automated CSV-based CTC capture method for pediatric brain tumor using the Abnova CytoQuest CR CTC isolation platform. With this platform, we processed PBMCs isolated from whole blood samples through the automated system to trap CSV-positive CTCs on to a chip. Captured cells are then stained for CSV and CD45 before automated scanning and detection to determine CTC yield per sample. RESULT: Ten patients were consented on the study, nine patients (3 high grade glioma, 3 brain stem glioma, 1 pineoblastoma, 1 medulloblastoma, 1 atypical teratoid rhabdoid tumor) had an adequate samples for testing, of those 7 had CTC detected from PBMCs. Our results show that brain tumors patients can and do exhibit CSV+ CTCs. These findings do not distinguish between various types of brain malignancies as evidenced by positive CTC isolation in multiple types of gliomas and embryonal tumors. CONCLUSION: Overall, we present the first study of CTCs in pediatric tumors using an automated approach. This is a promising methodology for future tumor risk stratification and treatment response monitoring.