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

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

keywords

• Liquid biopsy
• Cancer
• Glioblastoma
• Differential scanning fluorimetry
• Machine learning
• Plasma profiling
• Biomarker
• Neurophysiopathology
• Neuro-oncology

abstract

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained us‐ ing 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 EGFR 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 mu‐ tation, MGMT, seems harder, and that post‐surgery monitoring using our approach is not conclu‐ sive in the 48 h that follow the surgery.

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