Intraoperative assessment of tumor margins during glioma resection by desorption electrospray ionization-mass spectrometry

Abstract

Intraoperative desorption electrospray ionization-mass spectrometry (DESI-MS) is used to characterize tissue smears by comparison with a library of DESI mass spectra of pathologically determined tissue types. Measurements are performed in the operating room within 3 min. These mass spectra provide direct information on tumor infiltration into white or gray brain matter based on N-acetylaspartate (NAA) and on membrane-derived complex lipids. The mass spectra also indicate the isocitrate dehydrogenase mutation status of the tumor via detection of 2-hydroxyglutarate, currently assessed postoperatively on biopsied tissue using immunohistochemistry. Intraoperative DESI-MS measurements made at surgeon-defined positions enable assessment of relevant disease state of tissue within the tumor mass and examination of the resection cavity walls for residual tumor. Results for 73 biopsies from 10 surgical resection cases show that DESI-MS allows detection of glioma and estimation of high tumor cell percentage (TCP) at surgical margins with 93% sensitivity and 83% specificity. TCP measurements from NAA are corroborated by indirect measurements based on lipid profiles. Notably, high percentages (>50%) of unresected tumor were found in one-half of the margin biopsy smears, even in cases where postoperative MRI suggested gross total tumor resection. Unresected tumor causes recurrence and malignant progression, as observed within a year in one case examined in this study. These results corroborate the utility of DESI-MS in assessing surgical margins for maximal safe tumor resection. Intraoperative DESI-MS analysis of tissue smears, ex vivo, can be inserted into the current surgical workflow with no alterations. The data underscore the complexity of glioma infiltration.

Keywords: ambient ionization; glioma; lipids; neurological smears; tumor infiltration.

Fig. 1.

Chemical predictions of disease state for smears 10 (glioma, 99% TCP), 24 (white matter, 37% TCP), 40 (gray matter, 15% TCP), and 43 (glioma, 69% TCP). Projections of the tissue smears (black objects) are imposed on the principal component analysis (PCA) score space created from a reference DESI-MS spectral library (17); green, gray matter; blue, white matter; red, glioma.

Fig. 2.

Signal intensity of 2HG (m/z 147) normalized to the total ion counts in smears 62–70 for case 9. Secondary axis shows TCP as estimated by pathology in the same smears.

Fig. 3.

Margin smears (n = 44). (A) Frequency of DESI-MS prediction of disease state vs. pathology. G, glioma; GM, gray matter; IT (nos), infiltrated tissue (not otherwise specified); WM, white matter. DESI-MS has no IT (nos) assignments because all smears were assigned. (B) Frequency of DESI-MS prediction of TCP using NAA vs. pathology. Percentages of tumor cells are categorized as low (<33%), medium (34–67%), and high (>67%).

Fig. 4.

Chemical predictions of disease state and tumor cell percentage. (A) Three-dimensional mapping of chemical predictions over MRI volume reconstruction for case 4 with overlaid stereotactic positions of smears 25–29. Stereotactic positions were registered digitally to the preoperative MRI using neuronavigation in the OR. Stereotactic positions are color coded by classification using PCA and linear discriminant analysis: green, gray matter; blue, white matter; red, glioma. (B) TCP predictions via linear regression using NAA for smears 25–29. Gray objects, natural log of the NAA signal intensity (normalized to the total ion count) vs. TCP for histologically defined reference specimens with a line of regression (black line); red objects, predictions of tissue smears. The equation of the regression line was calculated as y = –0.03x + 0.59 with a Pearson correlation r of –0.89. (C) Three-dimensional mapping of chemical predictions over MRI volume reconstruction for case 2 with overlaid stereotactic positions of smears 12–17. The stereotactic image for the biopsied tissue corresponding to smears 10 and 11 was not recorded. (D) TCP predictions via linear regression using NAA for smears 10–17.