Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry

• Published in: Cancer research (Chicago, Ill.) Vol. 72; no. 3; pp. 645 - 654
• Main Authors: EBERLIN, Livia S, NORTON, Isaiah, DILL, Allison L, GOLBY, Alexandra J, LIGON, Keith L, SANTAGATA, Sandro, GRAHAM COOKS, R, AGAR, Nathalie Y. R
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 01.02.2012
• Subjects: Antineoplastic agents; Astrocytoma - classification; Astrocytoma - diagnosis; Astrocytoma - metabolism; Biological and medical sciences; Brain Neoplasms - classification; Brain Neoplasms - diagnosis; Brain Neoplasms - metabolism; Diagnosis, Differential; Diagnostic Imaging - methods; Glioma - chemistry; Glioma - classification; Glioma - metabolism; Humans; Lipids - analysis; Lipids - chemistry; Medical sciences; Neurology; Oligodendroglioma - classification; Oligodendroglioma - diagnosis; Oligodendroglioma - metabolism; Pharmacology. Drug treatments; Reproducibility of Results; Sensitivity and Specificity; Spectrometry, Mass, Electrospray Ionization - methods; Tumors; Tumors of the nervous system. Phacomatoses; Human; Intracranial; Nervous system diseases; Imaging; Central nervous system disease; Exploration; Medical imagery; Lipids; Mass spectrometry; Intracranial tumor; Cerebral disorder
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/0008-5472.CAN-11-2465

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 ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray 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 more than 97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79% of tested features. Together, our findings offer proof of concept that intraoperative 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.