Researchers have created a new automated device that can diagnose glioblastoma, a rapidly advancing and currently untreatable brain cancer, in under an hour. Typically, individuals diagnosed with glioblastoma have an average survival span of 12 to 18 months.
Researchers at the University of Notre Dame have created a new automated device able to diagnose glioblastoma, a fast-growing and incurable brain cancer, in less than one hour. On average, glioblastoma patients live 12 to 18 months after being diagnosed.
This diagnostic tool utilizes a biochip that implements electrokinetic technology to identify biomarkers, specifically active Epidermal Growth Factor Receptors (EGFRs), which are often overexpressed in certain cancers like glioblastoma and are present in extracellular vesicles.
“Extracellular vesicles, also known as exosomes, are specialized nanoparticles released by cells. They are considerably larger—10 to 50 times the size of a molecule—and possess a slight charge. Our technology was specifically engineered to take advantage of these particle characteristics,” explained Hsueh-Chia Chang, the Bayer Professor of Chemical and Biomolecular Engineering at Notre Dame and the primary author of the study published in Communications Biology.
Researchers faced two main challenges: they needed to create a system that could differentiate between active and inactive EGFRs and to design a diagnostic technology that could reliably detect active EGFRs on extracellular vesicles extracted from blood samples.
To tackle these issues, researchers developed a biochip featuring a cost-effective electrokinetic sensor about the size of a ballpoint pen’s ball. Since extracellular vesicles are large, the antibodies attached to the sensor can form multiple connections with a single extracellular vesicle, enhancing the diagnostic’s sensitivity and specificity.
Synthetic silica nanoparticles indicate the presence of active EGFRs on the collected extracellular vesicles while emitting a strong negative charge. When active EGFRs are detected, changes in voltage signal the presence of glioblastoma in the patient.
This charge-sensing technique reduces interference often encountered with existing sensor technologies that rely on electrochemical reactions or fluorescence detection.
“Our electrokinetic sensor enables us to perform functions that other diagnostic methods cannot,” said Satyajyoti Senapati, a research associate professor of chemical and biomolecular engineering at Notre Dame and co-author of the study. “We can load blood samples directly without needing pretreatment to isolate extracellular vesicles since our sensor remains unaffected by other particles or molecules, significantly reducing noise and increasing disease detection sensitivity compared to existing technologies.”
The device comprises three components: an automation interface, a prototype portable machine that prepares materials for testing, and the biochip itself. Each test uses a new biochip, but the automation interface and prototype can be reused.
Running a single test takes less than one hour and requires just 100 microliters of blood. Each biochip costs less than $2 to produce.
While this diagnostic device is primarily designed for glioblastoma, the researchers believe it can be modified to detect other types of biological nanoparticles. This adaptability could facilitate the identification of various biomarkers related to other health issues. Chang mentioned that the team is investigating this technology for diagnosing pancreatic cancer as well as conditions like cardiovascular disease, dementia, and epilepsy.
“Although our method is not exclusive to glioblastoma, it was particularly suitable to start with this cancer due to its severity and the shortage of early screening options,” Chang stated. “We hope that improved early detection could enhance survival rates.”
Blood samples for testing this device were supplied by the Centre for Research in Brain Cancer at the Olivia Newton-John Cancer Research Institute located in Melbourne, Australia.
Alongside Chang and Senapati, other collaborators included former postdoctoral researchers Nalin Maniya and Sonu Kumar from Notre Dame; Jeffrey Franklin, James Higginbotham, and Robert Coffey from Vanderbilt University; as well as Andrew Scott and Hui Gan from the Olivia Newton-John Cancer Research Institute and La Trobe University. The National Institutes of Health Common Fund provided funding for this study.
