Liquid biopsies for early diagnosis of brain tumours: insilico mathematical biomarker modelling. Issue 193 (3rd August 2022)

• Record Type: Journal Article
• Authors: Blee, Johanna A.; Liu, Xia; Harland, Abigail J.; Fatania, Kavi; Currie, Stuart; Kurian, Kathreena M.; Hauert, Sabine
• Is Part Of: Journal of the Royal Society interface. Volume 19:Issue 193(2022)
• Publication Date: 2022-08-03
• Subjects: glioblastoma multi-forme (GBM) -- mathematical modelling -- biomarkers -- glial fibrillary acidic protein (GFAP) -- brain tumours -- cancer; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2022.0180
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : Brain tumours are the biggest cancer killer in those under 40 and reduce life expectancy more than any other cancer. Blood-based liquid biopsies may aid early diagnosis, prediction and prognosis for brain tumours. It remains unclear whether known blood-based biomarkers, such as glial fibrillary acidic protein (GFAP), have the required sensitivity and selectivity. We have developed a novel in silico model which can be used to assess and compare blood-based liquid biopsies. We focused on GFAP, a putative biomarker for astrocytic tumours and glioblastoma multi-formes (GBMs). In silico modelling was paired with experimental measurement of cell GFAP concentrations and used to predict the tumour volumes and identify key parameters which limit detection. The average GBM volumes of 449 patients at Leeds Teaching Hospitals NHS Trust were also measured and used as a benchmark. Our model predicts that the currently proposed GFAP threshold of 0.12 ng ml −1 may not be suitable for early detection of GBMs, but that lower thresholds may be used. We found that the levels of GFAP in the blood are related to tumour characteristics, such as vasculature damage and rate of necrosis, which are biological markers of tumour aggressiveness. We also demonstrate how these models could be used to provide clinical insight.