Clinical diagnostic tests use a lot of different approaches to diagnose diseases. One area that is gaining ground is the so-called liquid biopsy, which typically uses next-generation sequencing to identify cancer cells in the blood.
Researchers at the University of Strathclyde in Glasgow, Scotland, have potentially developed a blood test for brain cancer using high-throughput attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy with machine learning. So far, it has been able to differentiate cancer and control patients at a sensitivity and specificity of 93.2% and 92.8%, respectively. They published their research in the journal Nature Communications.
“This is the first publication of data from our clinical feasibility study and it is the first demonstration that our blood test works in the clinic,” said Matthew J. Baker, Reader in Strathclyde’s Department of Pure and Applied Chemistry and chief scientific officer of ClinSpec Diagnostics, which spun out of the university in February and is commercializing the test.
Baker added, “Earlier detection of brain tumors in the diagnostic pathway brings the potential to significantly improve patient quality of life and survival, whilst also providing savings to the health services.”
There has been increased public attention to brain tumors, such as glioblastoma, since Arizona Senator John McCain died in 2018. Glioblastoma is an aggressive form of brain cancer, making up 16% of all brain malignancies. It is aggressive, spreading throughout the brain, but typically does not metastasize outside the brain. It is difficult to treat, essentially incurable. Early detection, before it spreads, would increase the odds of successful treatment.
“Diagnosing brain tumors is difficult, leading to delays and frustration for lots of patients,” said Paul Brennan, senior clinical lecturer and consultant neurosurgeon at the University of Edinburgh, a partner in the study. “The problem is that symptoms of brain tumor are quite non-specific, such as headache, or memory problems. It can be difficult for doctors to tell which people are most likely to have a brain tumor.”
The test, if approved for clinical diagnostic testing, would allow physicians to determine if patients with the nonspecific symptoms should be prioritized for brain imaging, which could result in faster access to treatment.
In their tests, the researchers analyzed samples from a cohort of 104 patients. The blood test identified patients with brain cancer from healthy patients correctly 87% of the time.
The test, which uses ATR-FTIR spectroscopy, is relatively simple, does not use radioactive labels, is non-invasive, and non-destructive. It can characterize the biochemical profile of a sample without significant sample preparation. It uses infrared light to determine a specific biochemical fingerprint.
In these sorts of tests, the biggest limitation is the internal reflection element (IRE), usually part of these systems. It is high cost, which limits its use for high-throughput testing. However, the research team developed a disposable silicon IRE that is significantly less expensive and allows for rapid preparation and analysis of multiple samples.
The team also investigated a 724 retrospective patient cohort using their system. The cohort contained a variety of primary and secondary brain cancers in addition to healthy control patients. These helped program the algorithms via machine learning, then the patient cohort was on a group of 104 patients. Within that cohort, 12 patients were identified as having cancer: four GBM, three anaplastic astrocytoma, two oligoastrocytoma, one medulloblastoma, one ependymoma and one gliosarcoma.
The authors wrote, “A simple test for the early diagnosis of brain cancer in primary care has the potential to make dramatic impacts on patient survival and quality of life, whilst also saving resources and costs within health services across the world. Analysis of blood serum using ATR-FTIR spectroscopy is a technique advancing towards the clinic, aiming to fill the void as a triage tool to help inform GP referral decisions.”