Classifying human brain tumors by lipid imaging with mass spectrometry
Cette étude évalue la faisabilité d'une technique d'imagerie par spectroscopie de masse pour permettre, au cours d'une opération chirurgicale, la détermination du sous-type et du grade d'un gliome
Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here we illustrate how gliomas can be rapidly classified by desorption electrospray mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was performed on thirty-six human glioma samples, including oligodendroglioma, astrocytoma and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Grey and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade and concentration features generated with lipidomic data showed high recognition capability with >97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 81% of tested features. Together, our findings offer proof of concept that intra-operative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors.
Cancer Research , résumé, 2011