Patient-Specific Chip Predicts How Glioblastoma Patients Respond to Treatment

Researchers led by the Korea Advanced Institute of Science and Technology (KAIST) have built a patient-specific chip that recreates a glioblastoma patient's tumor cells alongside their blood vessel environment, and can predict how the patient will respond to treatment. The team, led by Song Ih Ahn of KAIST's Department of Mechanical Engineering with collaborators at Sungkyunkwan University, CHA Bundang Medical Center and CHA University, published the results in the journal Small.

Glioblastoma is one of the most lethal brain tumors, hard to treat because cancer cells spread rapidly into normal brain tissue and tumor characteristics differ from patient to patient. The blood-brain barrier normally blocks harmful substances in the blood from reaching brain tissue, but it also blocks anticancer drugs, so not enough of a drug reaches the tumor. This barrier changes when glioblastoma develops, and how much it changes varies by patient, which is one reason the same drug can work differently in different people. Existing prediction methods rely mainly on tumor genetics and biomarkers, which cannot capture a patient's specific vascular environment or drug response.

To address this, the team built a microfluidic chip that includes not only a patient's tumor cells but also the vascular barrier the drug must cross, co-culturing patient-derived glioblastoma cells with brain vascular endothelial cells and astrocytes, in a design that can also accommodate perivascular and immune cells.

Using tumor cells from three glioblastoma patients who all showed the same result on the standard MGMT promoter methylation biomarker test, and were therefore expected to respond similarly, the team built patient-mimicking chips and applied the standard drugs temozolomide and bevacizumab. Both the vascular barrier characteristics and the drug responses differed from patient to patient on the chip, closely matching the patients' actual clinical courses.

Song Ih Ahn said the platform could eventually be validated in a larger patient population and developed into a preclinical tool for personalized treatment and new drug development, testing several drugs on a chip made from a patient's own tumor cells to select the most promising option in advance.