classifying human brain tumors by lipid imaging with mass ......molecular and cellular pathobiology...

2012;72:645-654. Published OnlineFirst December 2, 2011.Cancer Res Livia S. Eberlin, Isaiah Norton, Allison L. Dill, et al. SpectrometryClassifying Human Brain Tumors by Lipid Imaging with Mass

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Molecular and Cellular Pathobiology

Classifying Human Brain Tumors by Lipid Imaging with MassSpectrometry

Livia S. Eberlin1, Isaiah Norton2, Allison L. Dill1, Alexandra J. Golby2, Keith L. Ligon3, Sandro Santagata3,R. Graham Cooks1, and Nathalie Y.R. Agar2

AbstractBrain 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 canenable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate howgliomas 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 gliomasamples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades andvaried tumor cell concentration. Gray andwhitematter from glial tumorswere readily discriminated and detaileddiagnostic information could be provided. Classifiers for subtype, grade, and concentration features generatedwith lipidomic data showed high recognition capability with more than 97% cross-validation. Specimenclassification in an independent validation set agreed with expert histopathology diagnosis for 79% of testedfeatures. Together, our findings offer proof of concept that intraoperative examination and classification of braintissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previouslyunavailable information to rapidly guide surgical resections that can improve management of patients withmalignant brain tumors. Cancer Res; 72(3); 645–54. �2011 AACR.

Introduction

Critical decisions in medicine often depend upon discrim-inating normal from diseased tissue. For over a century, one ofthe central methods for identifying pathologic processes hasbeen the microscopic review of tissue sections by the trainedeye of human pathologists. This paradigm has been simple yetpowerful and has allowed for tremendous advances in theunderstanding, classification, and treatment of many humandiseases. Over the last fewdecades, newermodalities have beendeveloped to further enhance the capabilities of microscopy-based tissue diagnosis, particularly for the evaluation oftumors. These tests however remaining largely nonquantita-tive, require days or even weeks to complete and often assessvery specific questions rather than provide comprehensiveanalytical information. Newmodalities could further empower

the practice of surgical pathology, and assist in defining theextent of a tumor (1), and its relationship to surroundingnonneoplastic tissue (2–5). Such new modalities will need tobe applicable in real-time, and be validated using standardhistopathology evaluation.

Among the most useful ancillary tests in molecular pathol-ogy are those that detect changes in nucleic acids that are at thefoundation of tumorigenesis: DNA point mutations and chro-mosomal translocations (6, 7). Proteomic information is alsoused regularly in pathology, albeit in a low throughput mannerusing immunohistochemistry. Mass spectrometry imagingmethods have been increasingly applied to cancer researchdue to its ability to simultaneously map the distribution oflarge populations of proteins, a feature that is potentially ofenormous diagnostic and prognostic value (8). The lipid con-stituents of tissues, however, remain entirely unused in diag-nostic practice. Extensive investigations have established thatthe lipid composition of tissues can vary with disease state (9).A number of studies focus on themolecular changes that occurin cells, signaling the beginning of malignancy and providing ameans for early detection and treatment intervention (10, 11).These lipidomic findings emphasize the importance of deter-mining the composition of lipids in biological tissues for itsdiagnostic and prognostic value. Despite the diverse lipidcomposition of various tissues, lipidomic information has untilrecently been impractical to acquire. Desorption electrosprayionization-mass spectrometry (DESI-MS) and related MSimaging methods have now made acquisition of informationon specific lipids and their distribution in tissue highlypractical.

Authors' Affiliations: 1Department of Chemistry and Center for AnalyticalInstrumentation Development, Purdue University, West Lafayette, Indiana;2Department of Neurosurgery, and Department of Radiology, Brigham andWomen's Hospital, Harvard Medical School; and 3Department of Pathol-ogy, and Department of Medical Oncology, Brigham and Women's Hos-pital, and Dana-Farber Cancer Institute, Harvard Medical School, Boston,Massachusetts

Corresponding Authors: R. Graham Cooks, Department of Chemistry,Purdue University, West Lafayette, IN 47907. Phone: 765-494-5262; Fax:765-494-9421; E-mail: [email protected]; and Nathalie Y.R. Agar,Department of Neurosurgery, Brigham and Women's Hospital, HarvardMedical School, Boston, MA 02115. Phone: 617-525-7374; Fax: 617-264-6316; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-11-2465

�2011 American Association for Cancer Research.

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DESI-MS is one of a recently developed group of ionizationtechniques in MS in which samples are examined in theambient environment with minimal pretreatment (12, 13),therefore potentially applicable to real-time analyses. UsingDESI, direct lipid analysis from biological samples such astissue sections can be conducted rapidly and routinely (14).Chemical information obtained can be represented as 2-dimensional (2D) images recording abundances and spatialdistributions of specific ions (15). In a single analysis, DESI-MSallows the characterization and imaging of many lipids includ-ing fatty acyls, glycerophospholipids, glycerolipids, and sphin-golipids (14). DESI-MS imaging experiments have been con-ducted on human cancerous tissues including bladder (16),kidney (17), prostate (18), and testicular (19) cancers. In a pilotstudy using a limited number of specimens, DESI-MS imagingenabled discrimination of different WHO grades of astrocyto-mas based on lipid profiles (20).

Although imaging of individual ions can be used to distin-guish tumor from normal tissue, in most diseases the use ofmultiple ions enhances diagnostic classification and createsdiagnostic categories that often cannot be discerned by exam-ination of single ion data or by the visual examination of broadrange mass spectral data. Here, we report on the use ofmultivariate statistical approaches to provide correlationbetween lipidomic data acquired by DESI-MS imaging andglioma diagnostic information such as subtype, grade, andconcentration. A robust classification method was developed,validating the use of lipid profiles for the near real-timecharacterization of complex gliomas.

Materials and Methods

Tissue samplesAll tissue samples were cerebrum tumor tissue obtained

from the BWH Neurooncology Program Biorepository collec-tion, and analyzed under approved Institutional Review Boardprotocol, with informed written consent obtained by licensedneurosurgeons at BWH. Sample list is shown in SupplementaryTable S1. Samples were flash frozen in liquid nitrogen andstored at �80�C until sectioned at 14 mm thickness using aMicrom HM550 cryostat (Mikron Instruments Inc.), and thawmounted onto glass slides. The slides were stored in closedcontainers at �80�C; prior to analysis they were allowed tocome to room temperature, and dried under in a dessicator forapproximately 15 minutes.

DESI-MS analysisTheDESI ion sourcewas a lab-built prototype (15), similar to

a commercial source from Prosolia Inc. DESI-MS experimentswere carried out in the negative and in the positive ion mode.For more details, see Supplementary Data. The tissues werescanned using a 2Dmoving stage in horizontal rows separatedby a 200 mm vertical step. An in-house program allowed theconversion of the XCalibur 2.0 mass spectra files (.raw) into aformat compatible with the Biomap software (21) with whichspatially accurate images were assembled. Lipid species weretentatively identified based on collision-induced dissociation(CID) experiments tandem MS experiments and comparison

with literature data. DESI imaging of all tissue samples wererun in a randomized way using the same experimental con-ditions. Samples were obtained in 3 different sets, and each setincluded specimens of different subtypes and grades. Each setwas run in a random sequence, and under the same experi-mental conditions. Replicate data were recorded.

Histopathology analysisHematoxylin and eosin (H&E) stained serial tissue sections

were optically scanned using Mirax Micro 4SL telepathologysystem from Zeiss. Regions of heterogeneity within tissuesections were digitally assigned by the pathologist and com-pared with DESI-MS ion images. Tumor content was evaluatedby 2 neuropathologists (S.S. and K.L.) through examination ofan H&E stained tissue section, which was adjacent to the oneused for DESI-MS imaging studies. Specimens were evaluatedand scored visually with respect to their percentage of tumornuclei relative to the total nuclei in the sample. Samples werealso characterized and assigned a qualitative from normal toinfiltrative to solid, corresponding from low to high cellularity.

Statistical analysisSamples were divided into training and validation sets for

statistical analysis. Each class was defined with similar num-bers of specimens, and only specimens with clear diagnosis inaccordance with theWHO classification system (22) were used(agreement between 2 neuropathologists). A software tool wasdeveloped to enable the selection of regions of interest (ROI)based on visual inspection of the DESI ion image loaded inBiomap. More specifically, a rectangle with x, y coordinates isdrawn over an ion image to create a ROI, and the correspond-ing spectral data is extracted from the ROI, saved in a separatefolder, and converted for further analysis using ClinProTools2.2 (Bruker Daltonics). The entire mass spectrum acquired,fromm/z 200 tom/z 1000, was imported into ClinProTools andused for classification. ROIs were selected to be constituted ofapproximately 100 spectra each, so that each specimen weighssomewhat equivalently in a classification model, and to avoidincluding background signal from the glass. For heterogeneouscases, ROIs were selected based on neuropathologic evaluationand demarcation of ROIs within the tissue section, as visual-ized on the optical image of the H&E stained adjacent section.All ROIs were assigned by the neuropathologist for tumorsubtype, grade, and cell concentration.

Three distinct classifiers were built to recognize gliomasubtype, grade, and tumor cell concentration. Classificationmodels were built with the Support VectorMachine algorithm.For each classifier, the number of input peaks was automat-ically detected based on clustering of the peak rankings asdetermined by the SVM hyperplane separation with an upperlimit of 25 peaks to be used by the classifier.

The calculated models were used to classify spectra fromselected subregions for each validation sample. After classifi-cation, the classmembership was reported by ClinProTools forevery spectrum file with corresponding (x, y) spatial coordi-nates. To create the overlay image, corresponding H&E stainedoptical image, ion image, and class image were imported intoan image composition program (23), and manually aligned

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using rotation and scaling transforms. For more details, seeSupplementary Data.

Results

DESI-MS lipid profiles from glioma tissueDESI-MS imaging was carried out on 36 human glioma

samples including oligodendrogliomas, astrocytomas, and oli-goastrocytomas of different grades and varying tumor cellconcentrations. A complete description of the samples usedin this study, including information on glioma subtype, grade,recurrence, tumor cell concentration, EGFR copy numberstatus, 1p/19q status, and p53 immunoreactivity is providedin Supplementary Table S1. Although analysis was carried outin both the positive and negative ion mode, results aredescribed using the data obtained in the negative ion mode,in which the most significant discrimination among lipidprofiles was observed. Reproducibility was investigated byanalyzing different tissue sections of the same sample ondifferent days under the same experimental conditions (Sup-plementary Fig. S1A and S1B, Supplementary Information).The main lipid species observed in the examination of humanglioma tissue in the mass range of m/z 200 to 1,000 weresaturated and unsaturated fatty acids (FA), glycerophosphoi-nositols (PI), glycerophosphoserines (PS), plasmenyl glycero-phospho ethanolamines (plasmenyl-PE), and sulfatides (ST;ref. 20). Lipid profiles from the samples of the 3 different gliomasubtypes and of the samples of different grades within eachglioma subtype were evaluated. Different lipid profiles wereobserved from astrocytoma samples of grades II, III, and IV(Supplementary Fig. S2A–S2C). Grade II astrocytoma samplesshowed the presence of ST species, as well as PS and PI.Conversely, all grade IV astrocytomas analyzed consistentlyshowed absence of sulfatide species, with high abundance of PSand PI species. Interestingly, grade IV astrocytoma, whichdisplays very diverse cytomorphology on histologic sections(24), yielded reproducible DESI-MS lipid profiles in a range ofsamples with a lower total abundance of lipids than the low-grade astrocytomas.The WHO recognizes 2 grades of oligodendroglial tumors,

grade II for well-differentiated tumors, and grade III for ana-plastic tumors. Representative lipid profiles are presented inSupplementary Fig. S3A and S3B. Oligodendrioglioma is com-posed of neoplastic cells resembling oligodendroglia and char-acteristically harbors deletions in the 1p and 19q chromosomalarms. Oligoastrocytoma represents a mixed glial tumor that iscomposed of distinct neoplastic cells resembling both thosefound in oligodendroglioma and astrocytoma. The most abun-dant lipid species observed in the representative spectra ofsamples G41 (oligodendroglioma grade II) and G23 (oligoden-droglioma grade III) are plasmenyl-PE, PS, and PI. Free fattyacids such as palmitic acid FA(16:0),m/z 255.3, stearic acid FA(18:0),m/z 283.3, and arachidonic acid FA(20:4),m/z 303.2, andfatty acid dimers in the region of m/z 500 to m/z 650 wereobserved in both grades of oligodendrogliomas (16).The most abundant lipid species observed in the mixed

oligoastrocytoma grades II and III are the PS and PI (Supple-mentary Fig. S4A and S4B), with some similarity to oligoden-

drogliomas. Sulfatides were present in grade III oligoastrocy-tomas, suggesting an astrocytoma content of themixed tumor.Similar trends in the increase of relative abundance of PS(40:4),m/z 838.3 with increase of the oligodendroglioma grade areobserved in the mixed oligoastrocytomas, likely representingthe oligodendrioglial character of this mixed glioma. Further-more, grade II oligoastrocytoma presented higher relativeintensities of plasmenyl-PEs and glycerophosphoglycerols PG,when comparedwith grade III oligoastrocytoma, a trend not asclearly observed for any of the other subtypes of gliomas.

A comparison of the lipid profiles from different subtypes ofglioma samples withWHO grade III is shown in Fig. 1A–C. Thelipid profile of a grade III astrocytoma contains lipid species ofall PS, PI, and ST classes, whereas the grade III oligodendro-glioma shows a distinct profile of lipid species with PS(40:4)m/z 838.3, and PI(38:4) m/z 885.5 present at much higherrelative abundances than observed in the pure astrocytoma.The grade III oligoastrocytoma sample presents a profile of PSand PI similar to the oligodendroglioma profile, with thepresence of ST species at a lower relative abundance.

Morphologic heterogeneity within tumor types can takemany forms. One visually striking example of heterogeneouscellular elements are gemistocytic cells. On H&E stainedsections these cells seem to have fat/plump, eccentricallyplaced nuclei, and glassy cytoplasm. In our sample set, wehad 3 cases with a distinct population of gemistocytic cells: aGBM (sample G46), a grade II oligodendroglioma (sample G49),and a grade III oligoastrocytoma (G45). Interestingly, thesesamples containing gemistocytic cells all showed a distinctivelipid profile with abundant signals for a set of fatty acidsand fatty acid dimers. In the grade II oligodendrioglioma sampleG49,wenot onlydetected thephospholipid profile (m/z 700–900)characteristic of oligodendrogliomas (transitional betweengrade II and grade III; Fig. 2A–D), but we also noted a veryabundant signal of free fatty acid, specifically palmitic acid,m/z255, oleic acid,m/z 281 and arachidonic acid,m/z 303, and fattyacid dimers in the region of m/z 500 to m/z 650. This charac-teristic profile was observed from all samples harboring gemis-tocytes, showing the ability of DESI-MS to distinguish thepresence of this component in surgical specimens.

Mass spectrometry imagingOneof the advantages of imaging tissue sections byDESI-MS

in comparison with profiling the mass/intensity distributionsfrom tissue extracts is the preservation of morphologic fea-tures, providing means for visual differentiation of areas withheterogeneity and infiltration levels within the entire tissuesection, and for building accurate reference systems. Negativeion mode DESI-MS ion images of sample G23, an oligoden-droglioma grade III with 95% tumor cell concentration, areshown in Supplementary Fig. S5A–S5H. A relatively homoge-neous distribution of detected lipid specieswas observed in theion images obtained for this sample, as seen in the ion imagesof the most abundant ions.

In contrast, sample G33, a glioblastoma with an overall 80%tumor cell concentration, showsmarked heterogeneity accord-ing to DESI-MS ion images (Fig. 3A–F). As before, the isomerassignments are not completely unambiguous even though

Human Gliomas Are Classified by Subtype, Grade, and Cell Concentration

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tandem mass spectrometric data has been obtained on ions.Two regions with distinctive lipid profiles were observed fromthis section (Fig. 3G–H). Detailed histopathology evaluation ofan H&E stained adjacent tissue section (Fig. 3I) revealed thatthe region delineated with blue is of grade IV astrocytoma(GBM) with nearly 100% tumor cell concentration and focalvascular proliferation, whereas the region delineated with redis composed of mainly gray matter infiltrated by glioma cells,with lower tumor cell density (�30%). Indeed, the lipid profiledetected in the pure glioblastoma region was consistent withthe characteristic lipid profile of other glioblastoma samples,whereas a distinctly different lipid profile was detected in theregion of infiltrated gray matter. Figure 3J shows a plot ofintensity of discriminating lipid species as a function of theirlocation. The capability of DESI-MS imaging to clearly distin-guish these 2 regions based on the lipid composition show itspotential in detecting tumormargins and regions of higher andlower malignancy with an approximate spatial resolution of200 mm. Another similar example is shown in SupplementaryFig. S6A–S6F for sample G40, an oligodendroglioma grade II.

Statistical analysisAlthough qualitative association of lipid profile constitution

and spatial distribution with grade and subtype of gliomas can

be generally observed from high-density specimens, a moresuitable approach is to conduct statistical evaluation of theentire data set. The high dimensionality of 2D imaging datatogether with visual alignment to optical imaging of tissueprovides an opportunity to develop robust classification mod-els with the potential to streamline surgical pathology.

Classification models were built to recognize gliomasubtype and grade based on the WHO tumor classificationsystem (22). A preliminary model was also built to assesstumor cell concentration in low, medium, and high densitysubsets based on the overall histopathology evaluation of thespecimen. A list of the samples used to build the models isgiven in Supplementary Table S2. All 3 classifiers had overallrecognition capabilities more than 99%, and cross-validationmore than 97% (Table 1). Using the peak calculation criteriareported in the methods section, the software recognized128 peaks for the subtype dataset, 123 peaks for the gradedataset, and 136 for the concentration dataset as beingstatistically significant between m/z 200 to 1,000. Classifi-cation models were built from these peak lists with an upperlimit of 25 peaks to be used to distinguish classes (Supple-mentary Table S3). Although chemical identification of theions is not required for classification purposes, most of thelipid species selected by the classifier were identified based

Figure 1. Discrimination between subtypes of grade III gliomas is achieved through lipid profiles detected by DESI-MS. Representative negative ion modemass spectra from different grade III subtypes of human gliomas, (A) anaplastic astrocytoma, (B) anaplastic oligodendroglioma, and (C) anaplasticoligoastrocytoma. Insets show bright field optical images (400�) of the corresponding diagnostic permanent H&E section.

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on CID tandem MS experiments and comparison withliterature data (Supplementary Table S4).

Discussion

This work shows for the first time a comprehensive lipido-mic-based diagnostic approach for the classification of glio-mas, the most common primary human brain tumor. Classi-fication of DESI-MS imaging data showed both high recogni-tion capability and robust cross-validation. In addition, DESI-MS imaging allowed discrimination of tumor from infiltratedbrain hence delineating tumor margins. In all, the work pro-

vides a new modality to bridge neurosurgery and neuropa-thology and to help foster improved clinical decision making(25).

In developing classifiers, the training sets used specimensshowing classic histologic features. Complex specimens result-ing from recurrence were evaluated in the validation set.Despite the challenging nature of these test specimens, diag-nostic information for the validation set was in clear agree-ment with the histopathology evaluation. This included 15specimens evaluated for subtype, 17 specimens tested forgrade, and 20 specimens for concentration. For example,specimen G41_0, which was diagnosed by histopathologic

Figure 2. Distinctive lipid profiles with high abundance of fatty acids and dimers are detected for sampleswith gemistocytic cells. DESI-MS negative ionmodemass spectra of (A) sampleG49, oligodendrogliomagrade II with gemistocytes; (B) sampleG41, oligodendrogliomagrade II; (C) sampleG46, astrocytomagradeIV with gemistocytes; and (D) sample G27, astrocytoma grade IV. Insets show the magnified bright field optical images of the corresponding diagnosticpermanent H&E section, with the exception of A being from a frozen section.







evaluation as oligodendroglioma grade II/III with high tumorcell concentration (95%), was classified as 96% oligodendro-glioma by the subtype classifier and 33%grade II and 67%gradeIII by the grade classifier. Specimen G41_1 was tested andcorrectly classified as high tumor cell concentration (100%)by the concentration classifier. Sample G8, astrocytoma gradeIV was tested solely in the subtype classifier, which correctlyclassified it as 98% astrocytoma.

Only 11 discrepant results were noted for specimens of thevalidation set, which is noteworthy due to the heterogeneityand complexity of brain tumors. Note that discrepancy isdefined very strictly as meaning not simultaneously satisfyingall 3 criteria. A misassignment of concentration, or grade, orstage is interpreted as discrepant. Five of these were from thetumor cell concentration classifier. This classifier was builtconsidering the overall concentration of the specimens, usinglimited regions of approximately 100 spectra to define themodel. Although these ROIs were selected to minimize back-ground signal from the glass, the local tumor cell concentrationwas not taken into consideration, likewise for the ROIs testedfor concentration from the validation set. For the gradeclassifier, 3 results from 2 different cases showed discrepancywith clinical diagnosis. G48 was diagnosed as an astrocytoma

grade IV (GBM)with focal gliosarcoma, which is a GBMvariantcharacterized by biphasic tissue pattern with alternating areasdisplaying glial and mesenchymal differentiation (22). Theclassifiers indicated a subtype distribution between the 3different subtypes, and 90% of the tested spectra to be of gradeII character. The current classifiers have not been trained torecognize this subtype of GBM, which has a high content ofconnective tissue. Classification of specimen G49 also resultedin a priori discrepancy with diagnosis. The histopathologyevaluation of the specimen indicates an oligodendriogliomagrade II with very high concentration of gemistocytic cells. Theclassifiers recognize this specimen as a grade III astrocytomawith high tumor cell concentration, which would seem toreflect the highly gemistocytic area analyzed.

Specimen G20 was diagnosed as an oligoastrocytoma gradeII, although a prior resection at an outside hospital wasdiagnosed as a diffuse astrocytoma. FISH results for evaluationof 1p19q deletion showed 1p deletion in 16/50 nuclei and 19qdeletion in 4/50 nuclei, suggesting an oligodendroglial com-ponent to this tumor, but still do not explain the classificationresults as being 100% of oligodendrioglioma subtype. Thehistologic diagnosis of oligoastrocytoma is often challengingin the clinical setting, this glioma type is characterized by poor

Figure 3. Tumor heterogeneity and infiltration is assessed by DESI-MS imaging. Negative ion mode DESI-MS ion images of glioblastoma G33 showing thedistributionof (A)m/z788.8, PS(36:1); (B)m/z834.2, PS(40:6); (C)m/z885.4, PI(38:4); (D)m/z838.3, PS(40:4); (E)m/z857.3,PI(36:4), and (F)m/z303.3, FA(20:4).The color scale used in ion images is shown in F, which is normalized to the most intense (100% relative intensity, red color) peak in the mass spectra. Massspectra are shown from regions delineated with (G) blue and (H) red dotted lines, diagnosed as a region of 100% tumor cell concentration and infiltrated graymatter (30% tumor cell), respectively. Optical image of the H&E-stained adjacent section is shown in I, showing heterogeneous regions and the borderbetween thosemagnified. In J, a plot of the total abundance of the ionsm/z 834.2,m/z 885.4, andm/z 788.8 by the distance (mm) throughout the dotted blackline marked in H&E-stained optical image of I is shown.

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diagnostic concordance and reproducibility (26, 27). In aneffort to dissect the heterogeneity in the oligoastrocytomaclass, IHC was carried out to evaluate the status of p53 (26)in addition to considering 1p19q status. The overall results arereported in Supplementary Table S1, with 7/8 specimens testedto be positive for p53 expression. Figure 4A–B shows thedifference in p53 expression for the negative specimen G12

and for the positive specimen G21 (1p19q evaluation failed),indicating clearly the difference between the 2 specimensdiagnosed as oligoastrocytomas. A strong nuclear staining forp53 often correlates with TP53 mutation and is moretypical for low-grade astrocytomas, suggesting an astrocytic

character to these tumors. The heterogeneity in this subtypelikely explains some of the limitations of the subtype classifierin discriminating oligoastrocytomas from astrocytomas andoligodendriogliomas.

Because spatial information is retained and associated withthe spectra throughout the classification process, resultscan be visualized as a class image using a color code corre-sponding to each of the classes of a given classifier and alignedto the optical image of an adjacent section. This allows tovalidate results with expert histopathology evaluation of thetissue, and could become a visualization mean of mass spec-trometry-based diagnosis. G26 is a very homogenous grade IV

Table 1. Classification results for subtype, grade, and concentration classifiers applied to validation set ofsamples

Subtype Grade Concentration

%Overall recognition capability 99.3 99.7 99.0Cross-validation 97.9 99.2 97.5

Subtype Grade Concentration%

Name WHO diagnosis A OA O II III IV low med high

G6 A-IV; high 100 0 0 1 84 15G8 A-IV; recurrent A-IV 98 0 2aG26 A-IV; recurrent OA-III 100 0 0 0 0 99G28 A-IV 0 8 92G31 A-IV; recurrent A-III/GBM 0 0 100G32 A-IV; recurrent A-IV, High 94 0 6 16 33 51 48 9 43aG33 A-IV; high 45 5 49 21 36 43 1 1 98G48 A-IV; focal gliosarcoma 37 14 49 90 10 0 44 23 33G35 A-IV; high 19 20 61G30 A-IV-O; recurrent; high 100 0 0 0 5 95 4 14 82G29 A-IV-O; recurrent OA-III 69 0 31 32 36 32 77 17 6G5 A-IV-O; high (80%) 42 32 26 12 84 4G47_0 A-IV-O; recurrent O-II 43 17 40 65 0 35 1 0 99G47_1 A-IV-O; recurrent O-II 55 0 45 100 0 0 47 53 0G10 O-II, med 100 0 0 12 87 1aG40 O-II, infiltrated 20 3 78 94 6 0 12 85 3G42_0 O-II focal O-III; High 1 10 89G42_1 O-II focal O-III; High 82 18 0 0 3 97G43_0 O-II focal O-III; High 14 21 66G43_1 O-II focal O-III; High 9 15 76G49 O-II; gemistocytes, High 98 1 1 2 91 7 0 0 100G41_0 O-II/III; recurrent O-II, High 3 2 96 33 67 0G41_1 O-II/III; recurrent O-II, High 0 0 100G20 OA-II; residual A-II; med (30%) 0 0 100 90 5 5 89 2 9G13 OA-II-III; recurrent OA-II-III 2 98 0G45 OA-III; residual OA-III, High 0 97 3

NOTE: Gray areas correspond to specimens used in a corresponding training set; italicized results are in apparent disagreement withdiagnosis and further discussed; boldface type results highlight validation samples not used in any of the training sets.Cross-validation (leave-out 20%/10 iterations).Abbreviations: Astrocytoma (A), Oligoastrocytoma (OA), Oligodendroglioma (O).aG33 testedon a region of 696 spectra for subtype, andon region of 192 spectra for grade;G26 testedon region of 744 spectra for grade;G40 tested on a region of 900 spectra.







astrocytoma (GBM), which is clearly visible from the classimaging results (Fig. 5A), whereas G33 heterogeneity is clearlyobserved with the infiltrative region classified as a differenttissue type with oligodendrioglioma character (Fig. 5B).

The utility of visualizing class images is shown in Fig. 6 forsample G40. This is an oligodendrogliomagrade II with tumorcells infiltrating white matter. The region of heterogeneity isfurther investigated by placing a transparent overlay of clas-sification results on the optical image, revealing aggregates oftumor cells. This region seems as a weaving of low, medium,and high concentrations of tumor cells, and also presents witha potential region of progression to a higher grade. Tumorheterogeneity is common on histopathology evaluation ofgliomas, but the operative criteria for prognostic and thera-peutic decision making is based on the presence of high-gradeareas, even if present focally. DESI mass spectrometry analysisof surgical tissues has the potential to reconcile both byproviding detailed diagnostic information in near real-timeto guide therapy.

Many of the PI, PS, and ST species that were qualitativelyobserved tobedifferent between subtypes of gliomas (Fig. 1A–C)

were recognized by the subtype classifier. In addition, somepeaks not easily observed by visual inspection of the massspectra, such as PS (40:5), m/z 836.2, were recognized by theclassifier. Although the identity of most of these species is stillbeing investigated, a few are recognized as free fatty acids, suchas arachidonic acid, m/z 303.2. The 2 most significant peaksrecognized by the grade classifier werem/z 885.4, PI(18:0/20:4),andm/z 838.3, PS (18:0/22:4), which had also been qualitativelyrecognized as varying between different grades within eachsubtype, in terms of relative intensities (20).

This study validates the preliminary indications of discrim-ination between astrocytoma types by DESI imaging seen in apreliminary study. It also suggests that the analytical perfor-mance characteristics of the methodology are adequate to thechallenge of reproducibility and information content to allowclassification of tissue into the many subtypes represented bythe various combinations of type, grade, and concentrationencountered in the brain. This ruggedness in performancetogether with the relative simplicity of the imaging method-ology and especially the fact that the tissue is examinedwithout prior chemical treatment makes the approach highly

Figure 4. p53 expression in 2oligoastrocytoma samples. A,specimen G12, OA grade II(negative) and (B) specimen G21,OA grade III (positive). Differentexpression of p53 indicatesdifference between the 2specimens diagnosed asoligoastrocytomas.

Figure 5. Classification results canbe visualized as a class imageusing a color code correspondingto each class and registered to theoptical image of an adjacentsection. Subtype, grade, andconcentration classification for (A)sampleG26 (homogeneous) and (B)sample G33 (heterogeneous).

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encouraging for development as an intraoperative procedure.We anticipate that the recent development of nondestructivesolvent systems for DESI-MS imaging should enable evenhigher and more accurate correlation between MS lipidomicdata and tissue morphology (28). The molecular informationobtained byDESI-MS imaging analysis of glioma tissue can alsoprovide means of understanding the biochemical processesoccurring duringmalignancy.We anticipate that themetabolicvariations being captured by DESI-MS imagingmay contributeto a reevaluation of the traditional classification systems inmedicine.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

The authors thank for the assistance of Kristen K. Gill for tissue banking atBWH.

Grant Support

The work received support from the U.S. NIH (grant 1R21EB009459-01 to L.S.Eberlin, A.L. Dill, and R.G. Cooks); Brain Science Foundation and the Daniel E.Ponton fund for the Neurosciences and the NIHDirector's New Innovator Award(grant 1DP2OD007383-01 to N.Y.R. Agar); the Klarman Family Foundation (A. J.Golby).

The costs of publication of this article were defrayed in part by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received July 21, 2011; revised October 29, 2011; accepted November 20, 2011;published OnlineFirst December 2, 2011.

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classifying human brain tumors by lipid imaging with mass ......molecular and cellular pathobiology...

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