P10.18.A INSIGHT INTO LIPIDOMIC PROFILE OF BRAIN TUMORS: DETERMINATION OF LIPID MARKERS FOR DISCRIMINATION OF BRAIN TUMORS TISSUES THROUGH RAPID EVAPORATIVE IONIZATION MASS SPECTROMETRY
Authors: M Caffo, F Rigano, D Mangraviti, G Caruso, F F Angileri, A Germanò, V Barresi, L Mondello
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Surgical treatment is the main strategy of brain tumors. Similitude between infiltrative areas and normal brain during surgery is a challenge during surgery. Maximize debulking with decreasing morbidity influences overall and progression-free survival. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows real-time characterization of human tissue in vivo by analysis of the aerosol (“smoke”) released during electrosurgical dissection. This analytical approach generates chemical information according to the tissue and its pathology, related to lipidomics. Coupling of MS system with electrosurgical Knife is called iknife (intelligent knife). According to machine learning concept, a spectral library is built following analysis of histologically validated samples; chemometric software is capable to clusterize according to lipid profile. Unknown samples can be identified through automatic match with the database. We investigated the role of iKnife as a surgical tool able to distinguish in real time between different tissues, normal or tumoral. Acquired data could permit a maximal safe resection. “Smoke” provides a source of biological data such as metabolomic composition of the tumor and the neighboring tissue. The resulting mass spectrometric profiles are highly specific for each histopathology, and for tissue analyzed, allowing for identification as well as characterization on a level comparable with histopathological analysis. This analytical coupling creates new chemical information sets that describe the tissue and associated pathology. MATERIAL AND METHODS Iknife was applied to brain tumor samples of 93 patients and 7 non tumoral lesions (control tissues). Tissues collected during surgery and ex-vivo analysis was performed through iKnife. MS spectra were used for the building of statistical model through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). RESULTS: S pectral comparison between the analyzed tumor samples allowed to highlight differences between different tumors and grade. Such a visual inspection was the basis for the definition of four statistical classes: glioblastoma (n=29), metastasis (n=15), meningiomas (n=30) and non-tumor lesions (n=7). Accuracy higher than 91% was achieved for the identification of brain tissues. Glycolipids, phospholipids (PLs) and sphingolipids were identified as discriminant features. The increase of PLs in tumor tissues is the consequence of an enhanced synthetic activity during carcinogenesis, whereas the upregulation of glycoceramides in drug-resistant tumor is the way by which tumor cells can improve their survival. CONCLUSION The iknife can be a promising tool for differentiation normal brain parenchyma from tumor tissue, for distinction different brain tumors, with a potential analysis to discover new therapeutic targets.