Bioelectronics promise improved treatment for the single most aggressive brain cancer

Glioblastoma is the most aggressive, malignant brain cancer and, due to its location in the cerebrum and its patient-specific nature, it is notoriously hard to treat. Tumors often return, even when treated, and the disease presents an extremely unfavorable prognosis with a median survival rate of between 12 and 18 months. That is why the emergence of implantable bioelectronic devices, which work primarily by impeding cancer cell division, represents an exciting milestone in the development of new brain cancer treatment methods.

do Couto Lopes et al. highlighted the opportunities and challenges around creating patient-specific bioelectronic devices to enable real-time monitoring of the disease and, in response, to enact precise administration of treatment.

“The creation of biomedical devices that monitor and automatically respond to changes in the body — or closed-loop devices — is particularly difficult because suitable signal monitoring and therapeutic technologies must be joined together in a single device,” said author Christopher Chapman. “Here, we present four brain signals that are changed by this disease and highlight their potential and limitations for combined use as a monitoring solution.”

The researchers also emphasized the importance of engaging with patients early on in the development process, which can facilitate a significantly improved quality of life for them and help better inform health providers.

“[Implantable devices] can reduce anxiety between scans, which take place only every three to six months, and can enable on-demand administration of electrotherapies to treat disease when it is detected,” said Chapman. “As a clinical tool, this technology could provide much-needed information to clinicians to make rapid decisions on the best course of treatment.”

Source: “Closing the loop on glioblastoma: A roadmap towards developing bioelectronics for continuous monitoring of tumour state,” by Ester Clarisse do Couto Lopes, Joshua P. A. Daoud, Alexandra Collisson, Ariadni Georgiannakis, Joshua Killilea, Cédric M. John, Dimitrios Paraskevopoulos, and Christopher A. R. Chapman, APL Bioengineering (2026). The article can be accessed at https://doi.org/10.1063/5.0312207 .