molecular characterization of prostate cancer with ...in this report, we demonstrate how a...

Metabolism

Molecular Characterization of Prostate Cancerwith Associated Gleason Score Using MassSpectrometry ImagingElizabeth C. Randall1, Giorgia Zadra2,3, Paolo Chetta3,4, Begona G.C. Lopez5,Sudeepa Syamala3, Sankha S. Basu2, Jeffrey N. Agar6, Massimo Loda2,3,Clare M. Tempany1, Fiona M. Fennessy1,3, and Nathalie Y.R. Agar1,3,5

Abstract

Diagnosis of prostate cancer is based on histologic eval-uation of tumor architecture using a system known as the"Gleason score." This diagnostic paradigm, while the stan-dard of care, is time-consuming, shows intraobserver vari-ability, and provides no information about the alteredmetabolic pathways, which result in altered tissue architec-ture. Characterization of the molecular composition ofprostate cancer and how it changes with respect to theGleason score (GS) could enable a more objective and fasterdiagnosis. It may also aid in our understanding of diseaseonset and progression. In this work, we present mass spec-trometry imaging for identification and mapping of lipidsand metabolites in prostate tissue from patients with knownprostate cancer with GS from 6 to 9. A gradient of changes inthe intensity of various lipids was observed, which corre-lated with increasing GS. Interestingly, these changes were

identified in both regions of high tumor cell density, and inregions of tissue that appeared histologically benign, pos-sibly suggestive of precancerous metabolomic changes. Atotal of 31 lipids, including several phosphatidylcholines,phosphatidic acids, phosphatidylserines, phosphatidylino-sitols, and cardiolipins were detected with higher intensityin GS (4þ3) compared with GS (3þ4), suggesting they maybe markers of prostate cancer aggression. Results obtainedthrough mass spectrometry imaging studies were subse-quently correlated with a fast, ambient mass spectrometrymethod for potential use as a clinical tool to support image-guided prostate biopsy.

Implications: In this study, we suggest that metabolomicdifferences between prostate cancers with different Gleasonscores can be detected by mass spectrometry imaging.

IntroductionProstate cancer is the second most commonly diagnosed

cancer in men worldwide (1, 2). The standard of care fordiagnosis of suspected prostate cancer involves systematicbiopsy and histopathologic evaluation. Systematic biopsy,however, misses 21%–28% of prostate cancers and under-grades cancers in 14%–17% of cases (3). Multiparametric MR(mpMR) can detect suspicious lesions and clinically significantcancers [i.e., GS pattern 4 and/or tumors >0.5 cm3 (4, 5)].Image-guided biopsy methods such as in bore MRI-guided

transperineal (6) or MRI/ultrasound fusion have led toimproved sensitivity of diagnosis by biopsy (7). All biopsycores were labeled individually and taken to pathology forhistopathology-based diagnosis. There is currently no rapiddiagnostic tool available, such as the "frozen section" oftenused during surgery. All prostate biopsy cores are processed andanalyzed after the biopsy session, with final results typicallyavailable within several days to one week after the procedure,by which time there is little opportunity to resample. Themicroscopic examination of tissue is time-consuming and theGleason score (GS) scoring system used to classify prostatecancer has received much discussion and revision (1, 8–11). GScomprises two numbers, the most common followed by thesecond most common architectural pattern. For example, a GS7 could comprise (3þ4) or (4þ3). The former is known to beassociated with indolent disease as most of the pattern is of GS3, whereas the latter is likely to be more aggressive as most ofthe pattern is of the more aggressive GS 4 (1). The newGS grade-grouping classification introduced in 2016 attemptsto alleviate confusion with the traditional scoring system;however, shortcomings in GS classification remain. A lesssubjective method would improve diagnosis and risk stratifi-cation, and could identify features contributing to aggressiveprostate cancer.

There is a growing body of literature suggesting that metabolicchanges occur in prostate cancer development and could be usedas a measure of disease aggression and grade (12–18). Metabolic

1Department of Radiology, Brigham and Women's Hospital, Harvard MedicalSchool, Boston, Massachusetts. 2Department of Pathology, Brigham andWomen's Hospital, Harvard Medical School, Boston, Massachusetts. 3Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.4University of Milan, Milan, Italy. 5Department of Neurosurgery, Brigham andWomen's Hospital, Harvard Medical School, Boston, Massachusetts. 6Chemistryand Chemical Biology, Northeastern University, Boston, Massachusetts.

Note: Supplementary data for this article are available at Molecular CancerResearch Online (http://mcr.aacrjournals.org/).

Corresponding Author: Nathalie Y.R. Agar, Brigham and Women's Hospital,Harvard Medical School, 60 Fenwood Road, 8016-J, Boston, MA 02115.Phone: 617-525-7374; E-mail: [email protected]

doi: 10.1158/1541-7786.MCR-18-1057

�2019 American Association for Cancer Research.

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reprogramming is a hallmark of prostate cancer (11). Changes ingenetic regulators of lipidmetabolism result in upregulation of denovo lipid biosynthesis and fatty acid b-oxidation (12). Suchchanges are thought to facilitate tumor development by helpingto meet the energy demand for increased cell proliferation andgrowth. Prostate cancer cells are dependent on fatty acid oxi-dation (FAO) for ATP production and subsequent proliferationand survival (19). The carnitine cycle regulates fatty acid mito-chondrial import and export, and several components of thecarnitine system are upregulated in prostate cancer and ensuremitochondrial fatty acid supply (20). It has been suggested thatan increased capacity for de novo lipid synthesis and fatty acidoxidation provides a permissive growth environment withinthe peripheral zone of the prostate, within which approximate-ly 70% of prostate cancers occur (12, 21). Spatially resolvedmeasurement of metabolomic signatures from prostate tissuecould therefore enable a better understanding of how theseprocesses occur in situ, how tumor cells interact with surround-ing apparently nonneoplastic tissue, and how differences inmolecular content of different regions of tissue might supporttumor proliferation.

Mass spectrometry imaging (MSI) is a particularly attractivetool for the investigation of spatial differences in molecular tissuecontent. MSI can be applied as an untargeted technique,which is capable of detecting large numbers of molecules simul-taneously, without labeling or a priori knowledge. In addition,MSI preserves the spatial distribution of molecules in tissue,and numerous methods have been developed for metabolo-mic/lipidomic tissue imaging (22, 23). A number of MSI andmetabolomic studies have proposed biomarkers of prostate can-cer. Higher levels of linolenic acid and a-linolenic acid in ex vivotissue were found to be correlated with aggressive disease (24).Lysophosphatidylcholine was found with decreased intensity incancer compared with benign tissue by matrix-assisted laserdesorption/ionization time-of-flight mass spectrometry (MALDITOF MS) and lower levels seem to correlate with disease recur-rence after radical prostatectomy (25). Recently, the ratioof glucose to citrate was reported as a biomarker of cancer; lowerlevels of citrate were observed in prostate cancer possibly dueto higher levels of citrate oxidation as a result of downregulationof zinc uptake transporters (26). While these studies focused onmarkers for the discrimination of tumor and normal tissues,little is known about metabolic changes that may be associatedwith different grades of prostate cancer. As a heterogeneous andmultifocal cancer, prostate cancer stands to particularlybenefit from analysis by a spatially resolved technique; MSI hasthe potential to discriminate between tumor regions of differentgrade within the same tissue specimen, and characterize histo-logically benign tissue adjacent to the tumor to assess fieldcancerization (27–29).

In this report, we demonstrate how a high-resolution massspectrometry imaging method can be used to characterizemetabolic signatures associated with different GS, and a sec-ond, lower resolution technique could be used as a rapiddiagnostic tool. We demonstrate that a gradient of metabolicchanges, which increase with respect to cancer grade, can bedetected in prostate tissue. Comparable data were acquiredusing two different faster mass spectrometry methods, MALDItime-of-flight (TOF) MSI and liquid extraction surface analysis(LESA) MS, which could enable clinical translation of thismethod, for use alongside histopathologic analysis in inter-

ventional radiology pipelines to improve speed and accuracy ofprostate cancer diagnosis.

Materials and MethodsHuman tissue specimens from radical prostatectomy

Human prostate tissue specimens were obtained from the DF/HCC (GelbCenter) repository at theDana-Farber Cancer Institute(Boston, MA) and analyzed under Institutional Review Board–approved research protocol. The study was performed on tissue-banked, optimal cutting temperature (OCT)-embedded frozentissue specimens frompatients undergoing radical prostatectomy.Specimens (n¼10)were used for detailedMSI including: 2 xGS6,3 xGS (3þ4)¼ 7, 3 xGS (4þ3)¼ 7, and 2 xGS 9. Three specimenswere used forMALDI TOFMSI, including 1 xGS 6, 1 xGS (4þ3)¼7 and 1 x GS (4þ5)¼ 9. Analysis using ambient liquid extractionsurface analysis (LESA) mass spectrometry was performed on anadditional 4 samples including: 2 x GS (3þ3) ¼ 7, 1 x GS (3þ4)and 1 x GS (4þ3) ¼ 7. Full staging information for each of thesespecimens is included in Supplementary Table S1.

MALDI Fourier transform ion cyclotron resonance massspectrometry imaging

MALDI Fourier transform ion cyclotron resonance mass spec-trometry imaging (MALDI) FT ICR MSI was performed on 12-mm–thick sections of fresh frozen tissue mounted on microscopyglass slides. Sections were coated with a-CHCA (5 mg/mL in 70/30 methanol/water with 0.1% trifluoroacetic acid V/V) matrixusing a TM-Sprayer (HTX Imaging), with 4 passes of matrix at aflow rate of 0.17mL/minute, a track speed of 1,200mm/minute, atrack spacing of 2mm, and a nebulizing gas temperature of 75�C.MSI was performed on a 9.4 Tesla SolariX XR FT ICR MS (BrukerDaltonics), externally calibrated in electrospray ionization (ESI)-positive ion mode using a tuning mix solution (Agilent Technol-ogies). Images were acquired in positive ionmode (m/z 50–3000)at a lateral spatial resolution (i.e., pixel size) of 120 mm. Thetransient length was 0.4194 seconds and the resulting massresolving power was approximately 80,000 at m/z 798. Onlinecalibration was used to internally calibrate mass spectra fromevery location using the signal from heme (m/z 616.1776).

MALDI TOF MSIMALDI TOF MSI was performed using a Rapiflex MALDI

Tissuetyper (Bruker Daltonics). Tissue specimens were preparedunder the same conditions as for MALDI FT ICR MSI. Spectrawere acquired in positive ion mode, calibrated with a peptidestandard mixture, in the m/z range 100–1,500. The pixel sizewas 120 mm to match images acquired by MALDI FT ICR MSI.The laser frequency used was 1,000 Hz, and a total of 100 shotswere acquired per pixel.

MALDI MSI data analysisMALDI FT ICR MSI data were analyzed using SCiLS Lab

software (Bruker Daltonics). Data from each tissue section werecombined into a single SCiLS file. Data were reduced and binnedto a peak list of 481 discrete m/z values. Bisecting k-meansclusteringwas used to provide segmented images showing regionsof spectral similarity. Regions of interest were defined on the basisof the resulting segmentation map and receiver operating char-acteristic (ROC) analysis was performed to find ions whichdiscriminated between GS 7 and GS 9 tumor and normal tissue.

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m/z values with an AUC >0.75 were searched against publiclyavailable databases (Lipid Maps and the Human MetabolomeDatabase (HMDB); refs. 30, 31). Ions were preliminarily identi-fied if assignments were associated with Dppm < 2. MALDI TOFMSI data were also analyzed using SCiLS lab software.

Hematoxylin and eosin staining and histopathologicannotation

After MALDI MSI, samples were washed to remove the matrix(70/30 ethanol/water for 2 minutes, then 95/5 ethanol/water for1 minute) and stained with H&E, as described in (32). Hema-toxylin and eosin (H&E)-stained sections (both those analyzed byMALDI and un-sampled serial sections) were examined by 3expert pathologists to obtain a consensus GS and to delineatehistopathologically relevant regions. H&E microscopy imagesand MALDI ion images were coregistered using SCiLS Lab soft-ware to transfer delineated tumor regions onto the MALDI MSIdata.

LESA MS data acquisitionA further set of samples was analyzed by LESA (Advion)

coupled to an amaZon Speed ion trap mass spectrometer (BrukerDaltonics). Sampling locations were selected with spacing of 1mmin x and y dimensions to ensure therewas nooverlap betweenpoints. Themaximumnumber of sampling locationswas selectedper sample region (between 4 and 13). Tumor/normal regionswere determined by an expert pathologist on a serial H&E section.In this work, we used an extraction/ESI solvent system previouslyreported for lipid analysis (15/35/50 chloroform/methanol/iso-propanol, with 7.5 mmol/L ammonium acetate) (33). Solvent(1 mL) was aspirated from a reservoir into a conductive pipette tip.The tip relocated to a predefined position above the sample anddispensed 0.7-mL solvent onto the surface. The liquid microjunc-tion was maintained for 5 seconds to allow soluble analytes todissolve before 1 mL was reaspirated and injected into the massspectrometer via a nano-electrospray ionization source, with a gaspressure of 0.3 psi and a tip voltage of 1.4 kV. Mass spectra wereacquired for 1minute, in the positive ionmode, with anm/z rangeof 100–1,100 and the ion trap target tuned to m/z 700.

LESA MS data analysisMass spectrometry (MS) data were analyzed in DataAnalysis

(ver. 4.0; Bruker Daltonics). Partial least squares discriminantanalysis (PLSDA) was performed using Metaboanalyst (34). MSdata were normalized to the mean and autoscaling wasimplemented.

ResultsComparison of histopathology and MSI of prostate cancertissue

MALDI MSI was performed on a specimen of human prostateobtained from a radical prostatectomy. The tissue section com-prised one quadrant of the whole prostate and measured approx-imately 2.2� 1.5 cm.MALDI 9.4 Tesla FT ICRMSIwas performedwith a pixel size of 120� 120 mm, resulting in an imaging datasetcontaining 17,466 pixels, where each pixel contained a full massspectrumwith amass range fromm/z 50–3,000. AfterMALDIMSIanalysis, the matrix was washed from the slide and the tissue wasH&E stained. This slide, alongside an H&E stained (non-MALDIsampled) serial section, was evaluated by an expert pathologist

and assigned as aGS (5þ4)¼9 and further annotated to delineatehistologic regions (Fig. 1). Segmentation via bisecting k-meansclustering (k¼ 16) was performed on theMALDIMSI dataset anda segmentation map produced whereby pixels are color labeleddepending on spectral similarity. This segmentation map wascompared to theH&E image and different histologic features werecompared and correlated with different color clusters within thesegmented image (Fig. 1). Pixels from the tumor region wereassigned to yellow and light green clusters. The red cluster corre-lated with regions of atrophic glandular prostate (atrophy).Regions of normal prostate correlated with the bright greencluster. The dark blue/dark green cluster correlated with thecapsule. The purple/pink clusters correlated with extraprostaticadipose tissue. The region marked with an outline in purpleshould be noted because it was assigned the yellow/green clusters,but did not contain any tumor cells. One histology-based feature

Figure 1.

Comparison of histopathology and segmentation map produced byclustering of MALDI MSI data (k¼ 16) from a section of prostate tissue withGleason score 9. The tumor region is delineated in black, different coloredclusters correlate with different histopathologic features in the tissue, asindicated by the key inset.

Molecular Characterization of Prostate Cancer Using MSI

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noted in this region was the presence of siderophages and foamycells, which indicate resolving inflammation.

MALDI MSI of various GSs of prostate cancerMALDI MSI was performed on 5 human prostate tissue speci-

mens from radical prostatectomy. Tissue specimens with variousGS were included in the study to investigate how metabolites/lipids might change with respect to disease state (see Fig. 2).Microscopic images of the H&E-stained sections were examinedby a pathologist and regions of high tumor cell content wereoutlined (Fig. 3A). Microscopic images were registered with theMALDI MSI dataset and regions of interest (ROI) correspondingto the tumor were superimposed upon the ion images. A data-processing workflow was applied to the whole MS dataset; thisgenerated a segmentation map (with k¼ 8) whereby each pixel isassigned to adifferent color cluster that allowed regions of spectral

difference and similarity to be assessed (see Fig. 2B). Of note, thecolor assignment to a given cluster is arbitrary and therefore notcomparable from one analysis to the other, even though theunderlying spectral signature of a given cluster might compare.Overall, different clusters or groups of clusters seem to bespecific to tissue with different GS; the GS 6 tissue section isassigned to the light blue cluster, the GS (3þ4) ¼ 7 samples aremostly yellow and dark blue, the GS (4þ3)¼ 7 is mostly orangeand yellow, and the GS 9 sample, which contains regions of(5þ4) and (3þ4), is assigned a combination of orange, yellow,and dark blue. The region of GS (3þ4) in the overall GS 9sample is assigned to the yellow and dark blue clusters inagreement with the GS (3þ4) samples above, and the GS(5þ4) region is assigned to the orange cluster. This suggeststhat there are MALDI MS features that change with respect tothe GS score of the tissue, not only in tumor regions but also innormal tissue surrounding the tumor. The pale orange clustercorrelates well with tumor ROIs in the GS (4þ3) ¼ 7 and GS 9samples (Fig. 2B; shown in black). This suggests that there aremetabolomic differences between (3þ4) and (4þ3) tissuesthat can be detected by MALDI MS. The red cluster, whichdescribes the outer edge of each sample, is likely due tocontamination with the embedding medium (OCT); this clus-ter correlates well with synthetic polymer ions that weredetected near the tissue's edge.

Discriminative m/z between GS (3þ4) ¼ 7 and GS (4þ3) ¼ 7prostate cancer

Further analysiswas performed on the data to investigatewhichspecies were contributing to mass spectral differences betweentumor, normal and different GS of tissue. Receiver operatingcharacteristic (ROC) analysis was applied to the peak list of481 m/z values, to rank them in order of how well they classifiedbetween GS (3þ4)¼ 7 and GS (4þ3)¼ 7. ROC analysis providesa measure of the sensitivity and specificity of the intensity of anion todistinguish or classify between groups. Ionswith area underthe curve (AUC) >0.75 by ROC analysis were considered goodclassifiers that were detected with increased intensity in the higherGS. The AUC threshold of 0.75 produced a list of 56 m/z valuesthat were considered potential markers of higher grade disease.The 56 ions were searched against the Lipid Maps database andidentities were proposed. Assignments were accepted ifDppm < 2,resulting in a list of 31 lipids, (Table 1). A number of lipid speciesfrom the same lipid class were detected: four phosphatidylcho-lines (PC), four phosphatidic acids (PA), eight phosphatidylser-ines (PS), four cardiolipins, and five phosphatidylinositols (PI)were identified in this list. An example ion image of one of each ofthese lipid classes is displayed in (Fig. 4). None of these ions wascompletely specific to tumor tissue with all of them detected tosome degree in the histologically normal tissue. cardiolipins weredetected with higher intensity in tumor regions of GS (3þ4) ¼ 7(with cribriform cell pattern a presumed marker of aggressivedisease) andhigherGS, andwith increased intensity in the normaltissue in GS (4þ3)¼ 7 and GS 9 (Fig. 4A). Phosphatidylcholineswere detected with increased intensity in all tumor regionsregardless of GS (Fig. 4B). Phosphatidylserines, phosphatidicacids, and ceramide 1-phosphates (CerP) were not specificallyincreased in tumor regions, but had higher intensity over thewhole tissue section in GS (4þ3)¼ 7 and higher (Fig. 4C–E). PIswere detected with good specificity in the tumor regions of higherGS (Fig. 4F).

Figure 2.

Schematic description of experimental design and specimens used for eachstudy; MALDI FT ICR MSI tissue imaging was used to characterize lipids andmetabolites in tumor and normal tissues and assess differences betweendifferent Gleason scores (GS) in a test set of five specimens. MALDI FT ICRMSI was performed on a second set of five specimens consisting of the samerange of GS for validation. Themethod was translated using two faster MS-based techniques, MALDI TOF MSI and LESA MS. These were performed on afurther three and four specimens respectively, to validate the findings byMALDI FT ICR MSI and demonstrate the feasibility of a more rapid, MSmethod to characterize normal and tumor tissue in situ.

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MALDI MSI of validation set of prostate cancer specimensA further series of 5 prostate tissue specimens was analyzed by

MALDI FT ICRMSI as a validation set, to establishwhether similartrends were detected in a separate dataset. The same range of GSwas included in bothMALDIMSI analyses (see Fig. 5).MALDI ionimages of selectedm/zbased on the previous analysis demonstratethat broadly similar distributions were observed in the seconddataset (Fig. 5C–F). Segmentation of the data demonstrated thatthe GS 9 sample was assigned different clusters than all the otherspecimens of GS 7 and lower. More variation between specimenswas observed in comparison with the previous dataset, with nosingle cluster apparent for all tumor regions. Little differentiationbetween GS 4þ3 and lower was observed via segmentation of theentire dataset, possibly due to the large degree of variationbetween the GS 9 tumor regions and all other tissue regions/specimens. Segmentation of a smaller subset of one GS 6 and oneGS (4þ3) specimen demonstrated different clusters for the twodifferent grade tumors and differences between normal tissue(Supplementary Fig. S1).

Long-chain acylcarnitines are detectedwithhigh intensity inGS9 prostate cancer tissue

Two different long-chain acylcarnitines, palmitoylcarnitine,and stearoylcarnitine, were detected with high intensity in GS 9tumors, in one specimen of GS (4þ3)¼ 7 and one small region ofone specimen of GS (3þ4), (Supplementary Fig. S2). Acylcarni-

tines are involved in themitochondrial import of long-chain fattyacids for b-oxidation. Fatty acid oxidation is known to be upre-gulated in prostate cancer, a process that requires the activation ofacylcarnitines. The accumulation of long-chain acylcarnitines inhigh GS prostate cancer tissue has not, to our knowledge, beenpreviously reported. This finding is further supported by previousobservations of increased plasma concentrations of acylcarnitinein patients with prostate cancer (11, 35).

MALDI TOF MSI of prostate cancer specimensA rapid MALDI TOF MSI method was used to acquire data

from a further three prostate specimens, comprising GS 6, GS(4þ3)¼ 7 (90%Gleason 4), and GS (4þ5)¼ 9 (see Fig. 6). H&Eimages and corresponding segmentation maps of MALDITOF data are displayed in Fig. 6A and B. The segmentation mapdemonstrated that spectral differences between tumor andnontumor prostate tissue were detected as well as differencesbetween the GS. The yellow/orange cluster correlates well withtumor regions in the GS 7 and GS 9 specimens, but the clusterwas not found in the GS 6 specimen. Palmitoylcarnitine wasdetected with highest intensity in the GS (4þ3) specimen(see Fig. 6C). This specimen contained a large tumor and90% of the tumor was described by Gleason 4 type tissuearchitectures. This observation correlates well with MALDI FTICR data. Other example ion images are displayed in Fig. 6D–F,and generally correlate with high-resolution data. Ions with m/z782.7 and 749.1 were tentatively assigned as PC(34:1) and PS(32:2) based on the higher resolution FT ICR data, and demon-strate the same trends. Overall the data agree well, and althoughthe differences observed between GS in theMALDI TOFMSI dataare less obvious and there are fewer ions that discriminatebetween these specimens, this is to be expected of data that islower in spectral resolution. Data were acquired with the samepixel dimensions as the FT ICR MSI data acquired in Figs. 1, 3, 4,and 5; however, the shorter analysis time would enable higherspatial resolution data to be acquired in a more feasible time-frame. To acquire FT ICR data from a single tissue specimen tookbetween 2 and 6 hours, depending on the specimen size, and theresulting data were on the order of 100s of Gigabytes. In contrast,MALDI TOF MSI data acquired by the method described heretook approximately 30 minutes to acquire per section, and theresulting data was <10 Gigabytes. In a clinical setting, it may notbe necessary to acquire imaging data from a full tissue specimen,or at high spatial resolution. It is also possible that our MALDITOF MSI method could be further optimized to increase speedand translational capability.

LESA MS analysis of various GS prostate cancerA LESA MS method was developed to investigate whether a

faster technique could be used to acquire similar information toMALDI MSI, but with minimal sample preparation and increasedspeed/throughput for potential in situ diagnosis of prostate can-cer. Such a method could be used alongside traditional histopa-thology to guide clinical decisionmaking, in workflows similar tothose reported by this group previously (36–39). Four tissuespecimens [2 x GS 6, 1 x GS (3þ4) and 1 x GS (4þ3)] were usedfor LESA MS. Regions of high tumor cell content and normalprostate tissue were marked on an H&E-stained serial section andthese locations were transferred to the frozen section, (Supple-mentary Fig. S3). The slide was scanned and sampling locationswere selected within tumor and normal regions of each sample

Figure 3.

H&E and segmented MALDI FT ICR MS images of prostate cancer tissue. A,H&E images of tissue post-MALDI MS imaging, Gleason score (GS) isindicated inset and tumor regions as determined by histopathologicevaluation are marked in black, the tissue boundary (marking the areaselected for MALDI MSI) is marked in red. B, segmentation maps producedvia bisecting k-means clustering (k¼ 8) of MALDI MSI data, tumor regionswere transferred from registered H&E images.






(a total of 54 locations were analyzed over 4 tissue specimens).Spectra from normal and tumor tissue of all GS were qualitativelysimilar, with most ions detected in the phospholipid mass rangebeing detected with similar intensity across all samples. As such,multivariate analysis (PLSDA) was applied to see whether subtledifferences in spectra could be used to classify samples as tumor ornormal (Supplementary Fig. S4). A score plot of components 1and 2 show separate clusters corresponding to data points fromnormal and tumor samples, suggesting thatmetabolomic changesin tissue are detected by the LESA MS method and there ispotential for the method as a classification tool.

DiscussionA high spectral resolution MALDI FT ICR MSI method for the

analysis of lipids and metabolites in prostate tissue was devel-oped. This method was applied to a small number of humanprostate samples to facilitate an in-depth investigation of lipido-mic changes with respect to prostate cancer disease grade. A totalof 31 ions were identified as good classifiers (AUC >0.75) of GS(4þ3) and higher. When plotted as ion images, few of these ionswere found to be specific to tumorous tissue, with most beingdetected to some extent in the histologically benign tissue.A gradient of changes in lipid intensity were associated withGS, occurring in both tumor and normal tissue. Whether thesechanges in histologically benign cells occurred before the tumorgrew, or as a result of tumor cells changing the local chemicalmicroenvironment will be a question for further study. Theterm "field cancerization," describes molecular alterations occur-

ring in histologically normal tissue adjacent to tumors and hasbeen previously described in prostate cancer and others (27–29,40–42). A number of proteins includingMIC-1 and PDGF-A havebeen found with elevated expression in tumor-adjacent tissuesand it has been proposed that these secreted factors could pro-mote tumorigenesis leading to tumor multifocality (40, 43). Ourobservation of increasing lipid intensity in normal prostate tissueadjacent to tumor may indicate that metabolic alterations occurnot just in tumor cells, but in surrounding tissue as well. Thenontumor-specific nature of these molecules requires a multivar-iate statistical classification approach, which could enable classi-fication based on spectral "fingerprints" and subtle differences inthe proportions of different species.

Several phospholipids were increased in the tumor regions,consistent with increased cellular membrane content, and previ-ously published findings (12). Four cardiolipins were detectedand identified as classifiers of high GS. Cardiolipins are animportant component of themitochondrial membrane, and playroles in oxidative phosphorylation and apoptosis (44, 45). Car-diolipins were also detected in a recent metabolomics study ofcancerous prostate tissue usingMALDI FT ICRMSI, and regions ofhigh intensity were found to correlate well with tumor regions oftissue (45). This previous report did not include the GS of the 3tissue specimens analyzed, so we cannot compare whethersimilar intensity trends were observed; however, it is interestingthat the same lipid class was identified by two independentstudies. Phosphatidylserines, phosphatidic acids, and ceramide1-phosphates (8, 3 and 4 species of each respectively) were alsofound to be good classifiers of tumor grade; however, less contrast

Table 1. Classifyingm/z values between segmented tumor regions of Gleason scores (GS): GS (3þ4) ¼ 7 and GS (4þ3)¼ 7; only ions with ROC curve AUC > 0.75were considered for further identification

m/zmeas AUC (ROC curve) P (t test) Assignment m/zcalc Proposed formula Ion Dppm672.42175 0.896465 <0.001 PE(28:1(OH)) 672.4211 C33H64NO9PNa [MþNa]þ 0.97756.55066 0.884178 <0.001 PC(32:0) 756.5514 C40H80NO8PNa [MþNa]þ �0.98725.5567 0.875336 <0.001 SM(d34:1) 725.5568 C39H79N2O6PNa [MþNa]þ �0.14697.4776 0.842421 <0.001 PA(34:1) 697.4779 C37H71O8PNa [MþNa]þ �0.43776.59216 0.836056 <0.001 CerP(d44:3) 776.5928 C44H84NO6PNa [MþNa]þ �0.82942.62207 0.831284 <0.001 PS(48:9) 942.6219 C54H89NO10P [MþH]þ 0.18721.47803 0.826778 <0.001 PA(36:3) 721.4779 C39H71O8PNa [MþNa]þ 0.18780.54834 0.824187 <0.001 CL(80:9) 780.5482 C89H158O17P2 [Mþ2H]2þ 0.18943.56824 0.821017 <0.001 PI(44:9) 943.5695 C53H84O12P [MþH-H2O]þ �1.34969.58246 0.818404 <0.001 PI(O-42:6) 969.5803 C51H89O12P [Mþ2Na-H]þ 2.23745.47552 0.816761 <0.001 PS(32:4) 745.4763 C38H70N2O10P [MþNH4]þ �1.05723.49335 0.816391 <0.001 PA(36:2) 745.4755 C39H73O8PNa [MþNa]þ �0.21778.60876 0.813485 <0.001 CerP(d44:2) 778.6085 C44H86NO6PNa [MþNa]þ 0.33945.5838 0.812709 <0.001 PI(P-42:6) 945.5827 C51H87O12PNa [MþNa]þ 1.16995.59589 0.804375 <0.001 PI(P-44:6) 995.596 C53H91O12P [Mþ2Na-H]þ �0.11783.56787 0.803694 <0.001 CL(76:0) 783.5692 C85H166O17P2Na2 [Mþ2Na]2þ �1.70749.50769 0.802757 <0.001 PS(32:2) 749.5076 C38H74N2O10P [MþNH4]þ 0.12666.48358 0.802267 <0.001 CerP(d36:2) 666.4833 C36H70NO6PNa [MþNa]þ 0.42781.5531 0.799709 <0.001 CL(76:2) 781.5536 C85H162O17P2Na2 [Mþ2Na]2þ �0.64782.56549 0.799189 <0.001 PC(34:1) 782.567 C42H82NO8PNa [MþNa]þ �1.93747.49188 0.798069 <0.001 PS(32:3) 747.4919 C38H72N2O10P [MþNH4]þ �0.03613.34918 0.787268 <0.001 LPG(26:6) 613.35 C32H54O9P [MþH]þ �1.34973.60712 0.774293 <0.001 PA(54:11) 973.6083 C57H91O8PK [MþK]þ �1.21804.54663 0.77272 <0.001 LacCer(d30:2) 804.5468 C42H78NO13 [MþH]þ �0.21999.68384 0.77175 <0.001 MIPC(m38:1) 999.6856 C50H100N2O15P [MþNH4]þ �1.76998.68329 0.765729 <0.001 PS(50:6) 998.6845 C56H98NO10PNa [MþNa]þ 1.19734.56921 0.762519 <0.001 PC(32:0) 734.5694 C40H81NO8P [MþH]þ �0.26970.65326 0.759785 <0.001 PS(50:9) 970.6532 C56H93NO10P [MþH]þ 0.06914.59161 0.757626 <0.001 PS(46:9) 914.5906 C52H85NO10P [MþH]þ 1.10915.59595 0.756136 <0.001 PI(40:4) 915.5957 C49H88O13P [MþH]þ 0.27806.56427 0.753874 <0.001 CL(84:11) 806.5638 C93H162O17P2 [Mþ2H]2þ 0.58

NOTE: m/z were searched against Lipid Maps database and assignments were proposed; assignments were accepted if Dppm < 2.

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between tumor and normal was observed. This suggests thatnontumor-specific metabolic changes occur in the cells of tissuescontaining tumors of higher GS. Cardiolipins were detected withhigher intensity in the tumor regions of GS (3þ4) ¼ 7 withcribriform cell pattern and higher grade tumors. The highestintensity was detected from tumor regions of GS 9 tissue, andraises the possibility that these molecules may be indicative ofaggressive disease. Phosphatidylinositols were detected with high

intensity in GS (4þ3) and 9. High intensity of phosphatidylino-sitols in prostate tumor tissue was also observed by Wang andcolleagues (45), providing supporting evidence that our resultsare consistent with previous findings, but extend the scope of suchfindings to take into account theGS.Overall wefind that our studywas in agreement with other studies seeking to characterize lipidmetabolism in prostate cancer; however, we now extend thesefindings to consider the GS, and suggest that lipidomic changes

Figure 4.

MALDI MS ion images of varying Gleason scores (GS) of prostate cancer tissue, regions outlined in black correspond with tumor regions annotated by an expertpathologist. A,m/z 780.5483, which is identified as cardiolipin CL(80:9) (Dppm¼ 0.18). B,m/z 782.5655, which is identified as phosphatidylcholine PC(34:1)(Dppm¼ 1.93). C,m/z 745.4755, which is identified as phosphatidylserine PS(32:4) (Dppm¼ 1.05). D,m/z 697.4776, which is identified as phosphatidic acid PA(34:1) (Dppm¼ 0.43). E,m/z 776.5922, which is identified as ceramide 1-phosphate CerP(44:3) (Dppm¼ 0.82). F,m/z 943.5682, which is identified asphosphatidylinositol PI(44:9) (Dppm¼ 1.34).






may be associated with the cancer grade. This demonstrates asignificant step toward using mass spectrometry to not onlydistinguish between tumor and normal tissue, but to differentiatebetween different histopathologic features to provide clinicallyrelevant information.

Our results suggest thatmetabolic profiles of prostate tissue canbe linked to their GS, not only through analysis of tumor regionsbut also due to differences in normal tissue. This has implicationsfor in situ diagnosis of prostate biopsy tissue. Sampling errorduring systematic prostate biopsy, the standardof care for prostatecancer diagnosis, results in missed or inaccurate diagnoses. Ourresults warrant further study to fully characterize metabolic andlipidomic signatures of prostate tissue, both healthy anddiseased,

to assess the utility of mass spectrometry analysis as an in situdiagnostic tool.

Our rapid MSI and ambient MS methods were performed onseven further prostate tissue specimens. MALDI TOF MSI datawere acquired in a shorter time-frame and demonstrated simi-larities withMALDI FT ICRMSI data.We investigated the use of anambient method in which we were able to obtain mass spectrafrom tissue without any chemical sample preparation and theentire sampling and data acquisition process was performed inless than 2 minutes. Further method optimization may berequired to fully distinguish between different GS and differenttumor architecture; however, it was possible to classify tissue asnormal or tumor based on PLSDA of LESA MS data. These more

Figure 5.

MALDI FT ICR MSI of validation set of prostate tissue specimens.A, H&E image of tissue section post-MALDI analysis with tumor region, as determined by anexpert pathologist, outlined in black. B, Segmentation map of MALDI MSI data produced via k-means clustering (k¼ 8). C,m/z 780.5483, which is identified ascardiolipin CL(80:9) (Dppm¼ 0.18). D,m/z 697.4776, which is identified as phosphatidic acid PA(34:1) (Dppm¼ 0.43). E,m/z 782.5655, which is identified asphosphatidylcholine PC(34:1) (Dppm¼ 1.93). F,m/z 943.5682, which is identified as phosphatidylinositol PI(44:9) (Dppm¼ 1.34).

Randall et al.





rapid mass spectrometry–based analyses demonstrate the feasi-bility of clinical translation of the method. This study thereforeprovides a proof-of-concept that changes in cellmetabolismoccurin prostate cancer that can be correlated with GS and that MALDITOF MSI or LESA MS could be used to provide fast tissueclassification for in situ diagnosis. Validation of these results ona larger set of samples, as well as a comparison between fresh andfrozen specimens will be performed in future work.

ConclusionsIn this small but novel pilot study, we developed a high-

resolution MALDI MSI method for metabolomic/lipidomic anal-ysis and imaging of prostate tissue. We have identified severalclasses of lipids that can discriminate between low and high GS.

The strength of this study lies in the detailed analysis of a smallnumber of carefully curated and highly annotated specimens,which provide evidence that metabolic changes occur in prostatetissue during prostate cancer progression. These findings aresupported by other metabolic studies that have found many ofthe same classes of lipid to be detected with higher intensity intumor versus normal tissue. Here, we extend those findings todemonstrate that these metabolites also increase in abundancewith respect to GS. The gradient of metabolic changes detectedfrom prostate tissue suggests that it will be possible to classify anddiagnose prostate cancer based on mass spectral signatures. Thechanges detected in histologically normal prostate tissue are ofparticular relevance for in situ diagnosis of prostate cancer byneedle biopsy, where foci of disease are often missed. Data

Figure 6.

MALDI TOF MSI of three prostate tissue specimens with Gleason score (GS) indicated inset. A, H&E image of serial tissue section with tumor region, asdetermined by an expert pathologist, outlined in black. B, Segmentation map of MALDI MSI data produced via k-means clustering (k¼ 12). C,m/z 400.5, which isassigned as palmitoylcarnitine. D,m/z 782.7, which is assigned as PC(34:1). E,m/z 704.3, an unidentified ion, which exhibits lower intensity in the tumor. F,m/z749.1, which is assigned as PS(32:2).






obtained via rapid MALDI TOF MSI and ambient LESA MSdemonstrate promise for clinical translation. Future validationin a larger set of patient specimens in conjunction with develop-ment of computational data analyses for classification purposeswill next assess the clinical utility of the proposed method.

Disclosure of Potential Conflicts of InterestC.M. Tempany is a consultant/advisory board member for Profound

Medical. C.M. Tempany's spouse is a chief medical officer at SpringbankPharmaceuticals, reports receiving a commercial research grant fromInsightec, Inc., and has ownership interest (including stock, patents, etc.)in Springbank Pharmaceuticals. N.Y.R. Agar is a consultant/advisory boardmember for BayesianDx and InviCRO. No potential conflicts of interest weredisclosed by the other authors.

Authors' ContributionsConception and design: E.C. Randall, G. Zadra, M. Loda, C.M. Tempany,F.M. Fennessy, N.Y.R. AgarDevelopmentofmethodology:E.C.Randall, J.N.Agar,M. Loda,C.M. Tempany,N.Y.R. AgarAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): E.C. Randall, G. Zadra, J.N. Agar, M. Loda,C.M. Tempany, N.Y.R. AgarAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): E.C. Randall, P. Chetta, B.G.C. Lopez, S.S. Basu,M. Loda, N.Y.R. Agar

Writing, review, and/or revision of the manuscript: E.C. Randall, G. Zadra,P. Chetta, B.G.C. Lopez, S.S. Basu, J.N. Agar, M. Loda, C.M. Tempany,F.M. Fennessy, N.Y.R. AgarAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): P. Chetta, S. Syamala, C.M. Tempany, N.Y.R. AgarStudy supervision: C.M. Tempany, N.Y.R. Agar

AcknowledgmentsN.Y.R. Agar and C.M. Tempany receive support from the Ferenc Jolesz

National Center for Image Guided Therapy NIH P41-EB-015898. N.Y.R. Agaralso receives support fromNIH R01CA201469. C.M. Tempany receives supportfrom U01 HD 087211 and R25 CA 89017. E.C. Randall is in receipt of an NIHR25 (R25 CA-89017) fellowship in partnership with the Ferenc Jolesz NationalCenter for Image Guided Therapy at Brigham and Women's Hospital (P41EB015898). S.S. Basu receives support fromNIHTrainingGrant T32HL007627.The authors thank Dr. Catherine Rawlins, Nicholas Schmitt, and DanielDonnelly for technical advice with the MALDI FT ICR instrumentation. Theauthors thank Dr. Lavinia Stefanizzi and Dr. Nicolo' Fanelli for their assistancewith acquisition of specimens.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received September 28, 2018; revised December 19, 2018; accepted February6, 2019; published first February 11, 2019.

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2019;17:1155-1165. Published OnlineFirst February 11, 2019.Mol Cancer Res Elizabeth C. Randall, Giorgia Zadra, Paolo Chetta, et al. Gleason Score Using Mass Spectrometry ImagingMolecular Characterization of Prostate Cancer with Associated

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