Brain cancer is the leading cause of cancer-related death in children. Early detection and serial monitoring are essential for better therapeutic outcomes. Liquid biopsy has recently emerged as a promising approach for detecting these tumors by screening body fluids for the presence of circulating tumor DNA (ctDNA). Here we tested the limits of liquid biopsy using patient-specific somatic mutations to detect and monitor primary and metastatic pediatric brain cancer.

We demonstrate the feasibility and clinical utility of personalized mutation-based liquid biopsy for the surveillance of brain cancer in children. However, even with this specific and sensitive approach, we identified some potential false negative analyses. Overall, our results indicate that changes in ctDNA profiles over time demonstrate the great potential of our specific approach for predicting tumor progression, burden, and response to treatment.

Somatic mutations were identified in 3 ependymoma, 1 embryonal tumor with multilayered rosettes, 1 central nervous system neuroblastoma, and 7 medulloblastoma patients. The mutations were used as liquid biomarkers for serial assessment of cerebrospinal fluid (CSF) samples using a droplet digital PCR (ddPCR) system. The findings were correlated to the imaging data and clinical assessment to evaluate the utility of the approach for clinical translation.

We developed personalized somatic mutation ddPCR assays which we show are highly specific, sensitive, and efficient in detection and monitoring of ctDNA, with a positive correlation between presence of ctDNA, disease course, and clinical outcomes in the majority of patients. Conclusions: We demonstrate the feasibility and clinical utility of personalized mutation-based liquid biopsy for the surveillance of brain cancer in children. However, even with this specific and sensitive approach, we identified some potential false negative analyses. Overall, our results indicate that changes in ctDNA profiles over time demonstrate the great potential of our specific approach for predicting tumor progression, burden, and response to treatment.