Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status

• Published in: Cancers Vol. 15; no. 3; p. 760
• Main Authors: Eyraud, Rémi, Ayache, Stéphane, Tsvetkov, Philipp O, Kalidindi, Shanmugha Sri, Baksheeva, Viktoriia E, Boissonneau, Sébastien, Jiguet-Jiglaire, Carine, Appay, Romain, Nanni-Metellus, Isabelle, Chinot, Olivier, Devred, François, Tabouret, Emeline
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26-01-2023; MDPI
• Subjects: Accuracy; Algorithms; Aneurysms; Artificial intelligence; Biochemistry, Molecular Biology; Biomarkers; Biophysics; Brain cancer; Brain tumors; Cancer; Care and treatment; Chemotherapy; Denaturation; Diagnosis; DNA methylation; Epidermal growth factor receptors; Fluorimetry; Genetic aspects; Glioblastoma; Glioblastoma multiforme; Life Sciences; Lung cancer; Magnetic resonance imaging; Medical prognosis; Neurobiology; Neurons and Cognition; Oncology; Open source software; Patients; Plasma; Support vector machines; Surgery; France; United States > US; glioblastoma; neurophysiopathology; neuro-oncology; differential scanning fluorimetry; cancer; plasma profiling; biomarker; machine learning; liquid biopsy
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15030760

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate
alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.