Automated liquid biopsy detects brain tumor cells in children

Jan. 10, 2024
This tool could monitor treatment response and identify cancer relapse earlier.

Pediatric researchers at the University of Texas M.D. Anderson Cancer Center wanted to know whether a liquid biopsy tool that relies on detecting vimentin, a structural protein on the surface of many cancer cells, would work to capture and isolate CTCs in blood samples taken from children with CNS tumors. 

The researchers who published their study inCancersalso wanted to know whether automating the CTC capture method would improve their previously validated manual method. 

The researchers’ liquid biopsy approach captures cells with cell-surface vimentin (CSV) to isolate CTCs from patients’ blood, which can provide information about their cancer and monitor their ongoing treatment. 

The study authors had previously found that the manual liquid biopsy approach detected CTCs in adults with different types of cancer. In the current study, the researchers wanted to automate their method to boost its sensitivity and to capture CTCs from CNS tumors. 

The researchers enrolled 62 participants in their study: 58 children (median age 13 years) who were diagnosed with CNS tumors and four healthy adolescents (median age 16 years) who made up the comparison group. Forty-five of the participants with cancer had malignant tumors, including seven whose cancer had metastasized. 

The researchers took blood samples from all the participants to isolate and capture the tumor cells. After removing denser cells unlikely to contain CTCs, the researchers loaded the samples onto a machine that is equipped with a microchip. This microchip is coated with an antibody that recognizes CSV, which causes the CTCs to attach to it, but allows other types of cells to flow away. The cells on the chip were then stained so that they could be counted and identified. 

The automated method successfully captured CTCs in 50 of the 58 pediatric patients (86%). There were no significant differences in CTC detection based on patient characteristics, such as gender, age, disease status, or type of cancer therapy. 

Overall, the automated CSV-CTC capture tool was highly accurate in identifying patients with and without CNS tumor cells (meaning that the test was both sensitive and specific). The tool was also highly accurate in predicting the presence of CNS tumor cells, but it only predicted their absence about one-third of the time. 

Also, compared to the researchers’ previous manual CTC capture and identification process, the automated CTC isolation process increased the sensitivity of CTC detection rates by about 10% and decreased sample processing times.   

The research team also wanted to show that they could identify a specific mutation that has been associated with worse prognosis among patients with midline gliomas. When they analyzed the CTCs captured from patients with these types of tumors, they were able to detect this mutation among 75% of the samples.