Cerebrospinal Fluid Test Predicts Dangerous Side Effect of Cancer Treatment

By LabMedica International staff writers
Posted on 12 Mar 2025

Image: Among the proteins in the cerebrospinal fluid, the researchers identified biomarkers that can accurately predict the development of ICANS (Photo courtesy of Daiki Setoyama/Kyushu University)

In recent years, cancer immunotherapy has emerged as a promising approach where the patient's immune system is harnessed to fight cancer. One form of immunotherapy, called CAR-T-cell therapy, involves genetic modifications to a patient's T cells to enable them to target and destroy cancer cells. While this treatment has shown success, particularly in blood cancers, it is not without its risks. One of the severe side effects associated with CAR-T therapy is immune effector cell-associated neurotoxicity syndrome (ICANS), which causes inflammation in the central nervous system. Symptoms of ICANS range from mild issues like headaches and lethargy to more severe manifestations such as impaired consciousness, seizures, or even brain hemorrhages. The incidence of ICANS after CAR-T therapy is high, estimated at around 64%, but there has been no reliable method to predict its severity until now. Researchers have now discovered a method to predict this life-threatening side effect before it occurs.

Researchers at Kyushu University (Fukuoka, Japan) analyzed cerebrospinal fluid collected before treatment to identify proteins linked to harmful immune responses that affect the central nervous system following therapy. Published in Leukemia, the study could make cancer immunotherapy safer by helping doctors identify high-risk patients ahead of time, allowing them to implement early interventions or even prevent the condition. In this study, the team analyzed cerebrospinal fluid samples from 29 patients with B-cell non-Hodgkin's lymphoma before they underwent CAR-T therapy. Out of the cohort, 11 patients developed ICANS, while 18 did not.

The team identified 864 proteins across all spinal fluid samples, narrowing down the list to 46 proteins that displayed significant differences in concentration between patients who developed ICANS and those who did not. These proteins became potential biomarkers for predicting ICANS. The researchers pinpointed two key proteins: C1RL, which was elevated in patients who developed ICANS, and FUCA2, which had lower levels in those patients. When combined, the ratio of these two proteins proved to be highly accurate in distinguishing patients at high risk of developing ICANS from those at low risk. To validate their findings, the team tested the C1RL/FUCA2 biomarker on a second group of 10 patients undergoing CAR-T therapy, and in all cases, the protein ratio correctly predicted the risk of developing ICANS. However, the researchers noted that the small sample size means the results are preliminary and need further validation.

Beyond aiding in early detection and timely treatment, the researchers hope their identification of these biomarkers will allow for preventive measures before CAR-T therapy begins. For instance, since C1RL is involved in the complement system, which is known to trigger inflammation and may contribute to ICANS, patients identified as high-risk could be preemptively treated with drugs that inhibit this system. This predictive test could lead to a more personalized and safer approach to cancer treatment. Additionally, the research team plans to expand their investigation to see if these biomarkers can be applied to other blood cancers beyond B-cell non-Hodgkin's lymphoma. They are also exploring the potential for using easier-to-collect fluids, such as blood serum, to find more accessible biomarkers for treatment monitoring.

“Spinal fluid collection is an invasive and painful procedure, so most hospitals in Japan and other countries don’t routinely perform it before CAR-T therapy,” said Dr. Tomoko Nomiyama, co-first author and clinical technologist at Kyushu University Hospital. “If we can identify similar biomarkers in blood, our test would become a much simpler and more accessible tool for predicting ICANS.”