Integrating Rapid Evaporative Ionization Mass Spectrometry Classification with Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry to Unveil Glioblastoma Overall Survival Prediction
Abstract
Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with a median survival of 15 months. Despite advancements in conventional treatment approaches such as surgery and chemotherapy, the prognosis remains poor. This study investigates the use of rapid evaporative ionization mass spectrometry (REIMS) for real-time overall survival time classification of GBM samples and uses matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) to compare lipidomic differences within GBM tumors. A total of 45 GBM biopsies were analyzed to develop a survival prediction model for IDH-wild type GBM. REIMS patterns from 28 patients were classified with a 97.7% correct classification rate, identifying key discriminators between short-term (0–12 months) and prolonged (>12 months) survivors. Cross-validation with additional samples showed that the model correctly classified short-term and prolonged survival with 66.7 and 69.4% accuracy, respectively. MALDI-MSI was performed to confirm the discriminators derived from REIMS data. Results indicated 42 and 33 discriminating features for short-term and prolonged survival, respectively. Proteomic profiling was performed by isolating tumor regions via laser-capture microdissection (LMD) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Subsequently, 1387 proteins were identified, of which 79 were significantly altered. In conclusion, this study shows that REIMS rapidly predicts glioblastoma survival times based on lipidomic profiles during electrosurgical dissection. MALDI-MSI confirmed that these differences were specific to the tumor region in the glioblastoma sections. LMD-guided LC-MS/MS-based proteomics revealed significantly altered pathways between short-term and prolonged survival. This research, including the comprehensive predictive survival model for GBM, could guide tumor resection surgeries based on accurate real-time tumor tissue identification as well as provide insights into overall survival mechanisms, possibly related to therapy response.
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BibTeX
@article{Hendriks2025,
title = {Integrating Rapid Evaporative Ionization Mass Spectrometry Classification with Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry to Unveil Glioblastoma Overall Survival Prediction},
author = {Hendriks, Tim FE and Birmpili, Angeliki and de Vleeschouwer, Steven and Heeren, Ron MA and Cuypers, Eva},
journal = {ACS Chemical Neuroscience},
volume = {16},
number = {6},
pages = {1021–1033},
year = {2025},
publisher = {ACS Publications},
abstract = {Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near–real-time characterization of human tissue in vivo by analysis of the aerosol (“smoke”) released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.},
}