Anticancer Drug Testing- Proteome Signatures in Breast by MALDI, Immunostaining MRI-PET Biomarker

Recent Patents on Medical Imaging, 2011, 1, 000-000 1

2210-6847/11 $100.00+.00 © 2011 Bentham Science Publishers Ltd.

Taxotere Chemosensitivity Evaluation in Rat Breast Tumor by Multimodal Imaging: Quantitative Measurement by Fusion of MRI, PET Imaging with MALDI and Histology

Rakesh Sharma*,1,2

and Jose K. Katz1

1Departments of Radiology and Medicine, Columbia University, New York, NY 10032, USA

2Amity Institute of Nanotechnology, Amity University UP, Noida, UP, 201303, India

Received: 02 June 2010; Revised: 09 June 2010; Accepted: 27 June 2011

Abstract: Integration of imaging data with immunohistology is a new art. Increased PET and MRI image intensities on

rat breast tumor MRI-PET images were reviewed for possible correlation with tumor histology and MALDI imaging

tumor characteristics in the light of recent inventions and patents. Increased signal intensities of intracellular (IC) sodium

MRI and flouro-2-deoxy-glucose utilization by PET from apoptosis protein rich MALDI visible regions of tumors were

positively correlated to chemosensitivity of Taxotere. MCF-7 cancer cell line induced rat tumor MRI-PET images and

histology digital images were compared for correlation in pre- and post taxotere treated tumors. For MALDI imaging,

iterated protein ion mass spectrometry peak analysis was done using data from laser raster over tumor slices in sequence

and 3D tumor volume was simulated for specific peak(s) distribution. A criterion was developed to evaluate malignancy

by histology and MRI-PET imaging. For correlation, regression analysis was done using MRI-PET imaging, histology

and MALDI imaging data from MCF-7 tumor after 24 hours post-taxotere treatment. Apoptosis indices were calculated

by histostaining using pentachrome, feulgen and ss-DNA antibody assay. Review showed sodium MRI and PET signal

intensity distribution comparable and measurable in tumor tissue regions. In tumors, taxotere induced an increase in

IC-Na MRI signal with decreased tumor size and micro-PET showed FDG uptake increase with decreased tumor size than

that of control tumors after 24 hours. Histology features indicated tumor risk (high 'IC/EC ratio', high mitotic index and

apoptotic index), decreased tumor viability (reduced mitotic figures, reduced diploidy or aneuploidy and proliferation

index) after Taxotere treatment. These features in co-registered IC-Na, PET hypermetabolic and monoclonal antibody

(ss-DNA) sensitive regions showed 6% difference. MALDI imaging showed tumor specific protein ion species and their

distribution showed empirical correlation (limited visual match) with MRI-PET signal intensities but comparable match

with histology features. Recent patents strongly suggest the possibility of sodium MRI and PET multimodal imaging

integrated with MALDI-imaging as an non-invasive chemosensitivity assay to monitor the anticancer effect.

Keywords: MRI and PET integration with MALDI, apoptosis index, prostate tumor, validation, MALDI imaging, texotere chemosensitivity.

INTRODUCTION

Magnetic resonance imaging (MRI) combined with positron emission tomography (PET) microimaging multimodal technique was invented and recently emerged as multimodal molecular imaging tool in experimental tumor pharmaco-dynamic characterization. Matrix Assisted Laser Desorption/Ionization (MALDI) based imaging was invented and proposed as diagnostic technique and it is emerging now as imaging technique to visualize the cancer specific protein(s) for time-dependent monitoring of anticancer chemosensitivity by sodium MRI as research tool in cancer therapy [1]. However, MRI/PET visible tumor image characteristics and association with MALDI-imaging of tumor specific protein still remain a puzzle to correlate them with tumor physiology and histology characteristics due to difficulty of interpretating physical complexity of MRI-PET,

*Address correspondence to this author at the Center of Biotechnology,

Innovations And Solutions Inc., 3945 west Pensacola Street, #98,

Tallahassee, Florida 32304, USA; Tel: 18507027661; Fax: 18503395361; E-

mail: [email protected], [email protected]

signal physico-chemical complexity of MALDI signal and cytomorphic complexity of histopathology structural details of tumor [2]. In last 5 years, extensive efforts are made to develop time-of- flight MALDI (TOF-MALDI) as real-time fast imaging technique of accurate analysis of proteins and peptides with detail information of minute protein species by mass spectroscopy (up to nanomoles based on m/z ratio) by combining it with other variant mass spectroscopy SELDI, MALDI-LC, MALDI-TLC methods and modifying sample positioning, matrix composition and laser desorption/ absorption as represented in recent patents [3-13]. These techniques were of limited use to analyze protein composition as m/z peak intensities with or no information of protein distribution. In attempt to develop tissue MALDI imaging as ion distribution maps of selected m/z peaks of high intensities, we report Monte Carlos simulation technique to convert the MALDI peaks as points and display them as digitized simulated ‘m/z’ protein ‘ion peak maps’ at matched tissue locations on histology tissue sections coregistered with MRI-PET signal intensity distribution maps of tissue sodium-glucose uptake or oxygen contents. Currently, efforts are focused on integration or fusion of MRI-PET data from in vivo images and MALDI-histology-

2 Recent Patents on Medical Imaging, 2011, Volume 1, No. 2 Sharma and Katz

immunostaining data from ex vivo tissue slides to reconstruct the three-dimensional tissue volume with details of biophysicochemical, structural and molecular makeup of tissue. The present review focuses on physical principles and quantitative approach to explore the possibility of integration and fusion of in vivo and ex vivo tissue data.

Intracellular sodium MRI signal intensity increase in glioma, necrosis, and apoptosis was considered due to leaching out intracellular sodium after the membrane damage [1, 14]. Measurement of intracellular sodium by triple quantum, inversion recovery MRI imaging methods emerged as technique risk-free frominvasive infusion of Schiff reagents [15]. The

18F-Fluoro-deoxy glucose positron

emission tomography (18

FDG-PET) dynamic scanning indicated hyper-glycolytic regions in tumor while MRI generated static images [16].

In following sections, we review our study of MCF-7 injected rat breast tumor illustrating the static deformed PET registration with slice-by-slice MRI sections to demonstrate the point-wise match between MRI-PET signal intensities and protein MALDI peaks or images. A ‘quantitative MRI-PET-MALDI criterion’ was developed to validate and correlate the MRI/PET microimaging for tumor intracellular sodium signal intensities and PET active hyper-glycolytic regions, with MALDI protein peaks and histology tumor features. The assumptions of tumor cells were: 1. Loss of membrane sodium pump/symporter is associated with glucose pump and loss of oxygen (low oxidative phosphorylation makes high glycolysis); 2. In tumorigenesis, low oxidative phosphorylation high glycolysis apoptosis

necrosis cell proliferation cell death occurs in a sequence; 3. The events of tumorigenesis or drug antitumor action are detectable by in vivo oxidative phosphorylation and intracellular sodium (by MRI), in vivo high glycolysis and oxygen (by PET), apoptosis proteins (by MALDI protein peaks), ex vivo cytomorphometric changes of apoptosis, proliferation, necrosis, cysts (by histology); 3. Multimodal hybrid molecular imaging provides a finger print of tumorigenic kinetics and antitumor pharmacokinetics or therapeutic monitoring (Quantitative Theradiagnostics).

Relationship between sodium MRI and PET signal:

The sodium pump Na+/K

+ ATPase exchanges intracellular

sodium across cell membrane simultaneously with glucose transport. MRI/PET signal intensities represent the less known status of sodium and glucose transport [17]. The sodium MRI T1 signal is sensitive to intracellular sodium release while PET is sensitive to glucose uptake. The echo delay time (TE) applied during multiple flip-angle gradient-echo multi-slice imaging is a function of T1 or T2 signal intensity and concentration of sodium while biotransformation of

18FDG standard uptake value (SUV) in

PET is function of glycolysis.

Relationship between breast tumor protein

(proteomics by MALDI) and MRI-PET imaging: Protons act as coin with two faces (one sensitive to MALDI and other sensitive to MRI-PET). MALDI imaging is protein specific to metabolic disorder of rat tumor cells in various breast malignant tumor stages with following presumptions as recently described [18]:

• m/z of specific proteins act as molecular MALDI signatures of ‘m’ proton mass and ‘z’ ionic charge on proteins (bound with intracellular sodium) in tumors

• Sodium Magnetic Resonance Relaxation relationship with proteins (electrochemical protein-ionic forms or protons) serves as molecular signatures of sodium pump and sodium symporter protein in tumor cells (IC/zC ratio for unit mass proton)

• Intracellular sodium ions (bound with proteins) are visible at specific NMR frequency and single charged sodium bound protein ions or protons from protein generate distinct m/z peaks by MALDI

• Fusion of intracellular sodium (bound with proteins) MRI map with MALDI protein ion map displays empirical protein information

• Comparison of MRI signal vs MALDI signal gives matched characteristic with high: accuracy, repeatability, sensitivity

• At tumor specific physiological conditions of pH and temperature, specific protein molecule net charge and molecular mass ratio values predict specific protein(s) by MALDI.

• Standard peptide (proteins)samples of CHACA and HABA show specific m/z peak patterns. The breast tumor has specific hsp27/60, 14-3-3-annexin A2, phophoglycerate kinase-1, calreticulin, protein disulfide isomerase and phosphoproteins Pea 15 and Fabp5 [2,18].

• By Monte-Carlos simulations, the specific m/z peak(s) may be displayed as 3D reconstruct (image volume) or peak(s) spatial distribution in different regions of tumors [2, 19].

MALDI imaging principle: Protein detection probability from MALDI images displays several distinct protein m/z peak intensities after baseline denoising, peak intensity thresholding correction via apodization and regression or calculated normalized peak intensity with accuracy [20].

Reference acids (hydrocinnamic acid and sinapinic

acid) or (MH+ +Analyte M + Analyte-H

+) show high gas

phase or proton affinity (basicity) and they are desorbed and accelerated to a fix kinetic energy in a time-of-flight analyzer (a vaccum field free tube). In tube, protein ions are separated as a function of their m/z depending on velocity and time of flight as following:

m/z = 2Vt2/L

2(MALDI) ICMR-Na(MRI) SUV(PET) Eq 1

where m is the ion mass, z is number of charges, V is source potential, t is time of flight and L is length of TOF analyzer. For unit mass, (m=1)/z is proportional with intracellular sodium (IC) MR signal intensity and PET intensity (SUV or oxygen content from glycolysis). Single proton m/z, MRI, and PET intensities are known in standard calibration [20].

Due to possibility of multi-ionic charged proteins, laser pulse receives all charged protein ions with no delay and sends them to detector same time to detect all proteins in one step to generate high resolution m/z spectra or 2-dimensional ion density m/z maps of protein molecules from tumor tissue

Taxotere Chemosensitivity Evaluation in Rat Breast Tumor Recent Patents on Medical Imaging, 2011, Volume 1, No. 2 3

sample. Due to no exact internal standard, each peptide concentration is measured indirectly from peak amplitudes at different positions as representative of peptide size.

The ‘relative peptide concentration’ is calculated from MALDI peak as:

I’=1/ [I predicted intensity pI(s) ] Eq 2

Suppose ‘s’ is peptide sequence and I is intensity of MS peak.

MASCOT software for peptide mass finger printing was used to search protein analysis in terms of: 1. protein mass in range in Daltons (Da); 2. isoelectric pI of specific proteins [2]; 3. protein hit score and matched other common peak pattern of spectra.; 4. Narrowing down number of common sharp spectral peaks per protein and find matched spectral peak pattern common among proteins; 5. preprocessing for deconvolution, baseline correction and noise peak removal; 6. identification of m/z peak intensities; 7. narrow down search of protein specific m/z peaks [2].

MALDI in protein ion 3D display: After imaging, coregistered tumor sections at same histology match are placed on MALDI plate and laser beam rasterizes over tumor sections in MS spectrometer at different locations to generate an array of MS peak digitized points appearing as distribution maps of proteins as outlined in Fig. (1).

MATERIALS AND METHODS

Tumor propagation and Microimaging: Techniques were reviewed from patents (see Table 1) for MCF-7 induced tumor propagation inlocally raised rats and treated with Taxotere

® (Aventis Pharmaceuticals, Bridgewater, NJ)

similar to earlier report [20]. The sodium MRI (1 mm slice) and

18FDG-PET images of MCF-7 induced rats (n = 6) and

control rats (n = 3) were scanned with Animal Care and Use Committee guidelines in force at Columbia University as described in previous report [1, 21]. In brief, rats were placed inside 26 mm birdcage coil and imaged in 4.23 T clinical imager to generate T1, T2 parametric images. Sodium IR MRI T1 maps were obtained using multiple flip-angle gradient-echo multi-slice images (128 128 10, FOV = 25 25 1.8 mm). For T1 map, TR = 200 msec, TE = 4.5 msec, = {15°, 30°, 45°, 60°, 75°, 90°}) were chosen. For T2 map, TR = 4 sec, TE = {12, 24, 36, 48} msec) were chosen.

The T1 maps were generated by non-linear fitting the gradient-echo multi-slice images (T1 weighted images) to the following function:

T1w( ) = C0(1-e-TR/T1

) sin( )/(1-cos( ) e-TR/T1

) + C1, Eq 3

where, T1w( ) is the per-voxel measured signal at each flip angle, TR is the repetition time for the MR experiment, T1 is the fitted parameter, and C0 and C1 are constants used to account for baseline signal and measurement offset, respectively. T1 values were measured at each voxel on voxel-by-voxel T1 map.

The T2 maps were obtained by non-linear fitting the gradient-echo multi-slice images T1 weighted images to the following function:

T2w(TE) = C0*e-TE/T2

+ C1 Eq 4

where, T2w(TE) is the per-voxel measured signal at each echo-time, TE is the echo-time for the MR experiment, T2 is the fitted parameter and C0 and C1 are used to account for baseline signal and measurement offset, respectively.

After magnetic resonance imaging, each animal was immediately killed, perfused with saline and frozen in an ice block to minimize any postmortem protein degradation. The frozen tumor tissue was sectioned to 20 micrometers thick on a cryo-microtome (Leica) in the cranial-caudal direction.

Tumor sections from each segment in the blockface volume were prepared for MALDI-IMS acquisition. MALDI IMS images were taken and collected every 150 micrometers in an anterior to posterior fashion. The tumor tissue ion images were collected at lateral resolution of 150-300 micrometers, with each pixel represented by accumulation of 300 laser shots. MALDI spectrum of mass-charge ratio data was processed for spectral smoothing and base-line correction in MATLAB and ProTSData (Biodesix) software (see Fig. 1). MicroPET facility in Milstein Hospital building was used to demonstrate hyper- or hypometabolic tumor regions in Taxotere

® treated Sprague-Dowley rats after

injection of 100 Curie 18

FDG by intravenous route.

Technique Development for MALDI-IMS Data Acquisition

Mass spectrometer was tuned and controlled in its operations for TOF-MALDI MS spectroscopy mode and it was also used as the data source to acquire, process, store, and print. Most of the analog electrical signals reach the computer after analog-to-digital converter is used. In reverse order, digital signal can be converted to analog signal. However, in MALDI, transputer is used as digital device to convert its electrical signals in the form of pulses or proportional m/z peak intensities. A mass spectrum has m/z values (peaks) each showing peak height proportional to number of protein ions with unit charge. The m/z peak shapes from selected tumor tissue locations on MALDI slide (protein molecules) generate a set of electrical signals at preset voltage (checking is important in different Scanning modes). By manipulation of mass spectra data, accurate mass measurement was done (relevant peak sorting by thresholding at a certain peak height) to gather important m/z peaks and compare with calibrated reference peaks of reference CHACA and HABA calibrated compounds. By ‘match and try’ a set of peptides was selected to determine the protein make-up (proteomics finger print) using MOSCOT and Swiss library search for molecule identification as shown in Fig. (1) at the bottom.

Tumor tissue samples were prepared for MALDI data using techniques described in a previous study [1]. Collected tissue sections were transferred using rice paper to gold-coated MALDI target plates (Applied Biosystems Inc.) and spray-coated with a 25 mg/mL sinapinic acid matrix solution prepared in 60% acetonitrile, 0.5% trifluoroacetic acid. Approximately 10 mL of matrix solution were needed to produce a homogeneous matrix crystal layer. Matrix coated samples were then analyzed on a linear MALDI-time-of-flight mass spectrometer (Autoflex II, Bruker Daltonics Inc.) equipped with a Smartbeam™ laser operating at 100 Hz. Each peak was analog signal varied


from base line. First signal was digitized and simulated by Monte-Carlos approximated 3D reconstruction volume. An analog to digital converter (ADC) converted a continuous signal to series of digital pulses in which the voltage represented snapshots of the biochemical nature of molecules as analog signal taken at regular time intervals (discrete digital readings in Fig. 1). The voltage from ion detector varies as ions were introduced and focused. As each m/z value was focused, a peak ion current generated the voltage change proportional to each m/z amplitude or peak intensity on spectra.

3D Spatially Resolved MALDI-IMS Post-Processing

Image post-processing was performed to recreate spatially resolved 3D MALDI IMS data. The first step was to align the MALDI mass spectra to the targeting image used by the mass spectrometer. The MS spectrometer reports the

origin of MS spectra within the tumor field-of-view (FOV) in terms of internal motor coordinates. These tumor locations are used as training fiducials related to a targeting image(s) selected at the time of data acquisition. Pixel locations of the training fiducials in the targeting image were registered to their corresponding motor coordinates using an affine alignment. Tumor locations in internal motor coordinates within the mass spectrometer were transformed into image’s pixel space (spatial locations) by alignment via contour based registration of shape features in each image of the targeting images as reconstructed block face image (outline of the rat tumor section). The contours were manually highlighted by iterative closest point (ICP) algorithm to align the corresponding contours in targeting images. This transformation placed the targeting image coordinates into block face image coordinates. The slice location in z-direction or axial direction was annotated for each slice number and the thickness of each slice to append it with each

Fig. (1). Outline of MALDI-MRI-PET-immunostaining integration of digital tumor images is shown to generate a MALDI map of proteins

to highlight the m/z peak selection and peak digitization to reconstruct proteomics map and 3D tumor volume. After MRI-PET imaging,

tumor is excised and processed for histology and MALDI to generate a rasterized information of tumor cytometry (shown as 1-5 and a to g

regions) with corresponding (spectra 1,2.. n) of protein distribution in breast tumor tissue. Note the spatial information of protein ions in 3D

cube on right obtained by thresholding and baseline correction. The 3D tumor digital information is fused with MRI-PET images. See Fig.

(4) for better visualization.


2D block face coordinate to provide a 3D physical space location for each MALDI IMS spectra or a continuous transformation from the 2D motor coordinates to 3D animal specific coordinates.

Breast Tumor 3D Reconstruction Volume

Spatially resolved three dimensional volume reconstruction MALDI IMS images were reconstructed by post-processing steps. The ice-block was manually set across a stationary blade to section the rat tumor tissue at the interval of 1 mm distance and 5 micron thickness. Retrospective inter-slice registration was used to align consecutive blockface images. For each set of blockface images, the second slice was aligned to the first slice. The registered MRI image slice was used as the target blockface to align the next consecutive histology slice in the tumor volume. This process was iterated upon until all of the MRI images had been registered, and concatenating these together yielded an accurately reconstructed blockface volume.

Tumor Registration

Three dimensional MALDI-IMS and magnetic resonance images were correlated by geometrical deformation and rigid co-registration (tumor high signal intensities on images as solid object) of blockface volume (PET frames) to the magnetic resonance volume (3D slices) of tumor. The heterogeneous tissue nature of breast tumor prevents centroid registration large scale deformable changes in anatomy and deconvolution method cannot realign them. A six-degree of freedom rigid body (position and orientation) normalized mutual based registration was used to associate and align implicitly the blockface PET data (moving frames in ‘x’ angular direction) with magnetic resonance volumes (ascending image slices in ‘z’ direction) shown in Fig. (3).

Registration algorithms: IDL (Research Systems, Inc., Boulder, CO) on Pentium IV platform was used to use Automated Image Registration (AIR) algorithm and mutual information (MI) algorithm for following subsampling at co-ordinates 884, 442, 221, or 111 (XYZ subsampling) after prealignment by manual reorientation of tumor volumes selected in the ‘capture range’ as reported elsewhere [22]. Rigid geometric transformation was used using six SUV and MRI imaging characteristics without smoothing by AIR and MI algorithms for ‘convergence optimization’ method [23]. However, singular value decomposition (SVD) algorithm was used to estimate the registration errors to give “perfect” registration transformation matrix [24, 25]. This transformation was applied to a set of points spaced in and around the mouse tumor. The SVD-transformed set of points was then retransformed with the inverse of the transformation matrix arising from the multimodality registration to visualize tumor pixels. The mean Euclidean distance between these final points and the maximum distance between these points measured the registration accuracy and the “functional performance” of both algorithms [26].

Histology features and tumor evaluation criteria: A stereotactic MRI-PET match criteria was applied at different 16 locations of each tumor on histology slide to indicate different tumor features [27].

Registration and similarity match: Tumor was assumed as rigid body. Based on rigid body registration principle, first tumor anatomic features were segmented from a MRI data set. Intensity-based, pattern-matching algorithm was used for inter-slice registration based upon Normalized Mutual Information [28]. NMI was optimized for x-y translation and in-plane rotation to register sequential images in the reconstruction process. The specific implementation of the algorithm used in this paper was provided by the Insight Toolkit (http: //www.itk.org). It is important to note that registration via NMI requires no fiduciary systems [29].

Validation method: For validation of PET, MRI image intensities, biotransformation was optimized and registration methods were compared to the ground truth histology sections and registration accuracy was evaluated by root mean square (RMSE) as described earlier [21].

Imaging and histology data of tumor area with different cytomorphic features were compared by image processing using Optimas 6.5 and statistical correlation was calculated using PRISM 3.03 softwares.

RESULTS

Reviewed data showed that subcutaneous tumors measured 0.1 to 0.5 cm in diameter as shown in Fig. (2). The MRI and PET image acquisition systems generate images in different format so making intrinsic MRI, PET image registration difficult with blockface of tumor on microtome for histology. The blockface volume reconstruction was completely automatic. Normal rat images did not show hyperintense regions. The tumor regions on MRI and PET images were prealigned by manual reorientation using tumor shape as pseudo marker as shown in Figs. (2, 3). The breast tumor areas in five rats were selected to image the whole tumor mass in each. The volume ‘trimming’ removed the extra image points not common to both PET and MRI data sets from tumor. The rigid geometric transformation by AIR and MI algorithms generated ‘convergence optimization’of common data points of tumor visible on both MRI and PET images as shown in Figs. (1, 2). The singular value decomposition (SVD) algorithm estimated the registration errors up to 10.5% and generated registration transformation matrix by applying transformation to a set of points spaced in and around the breast tumor. The transformation matrix visualized the tumor pixels up to 1.5 mm. The mean Euclidean distance between these final points in fused coregistered images and the maximum distance between these points measured up to +10% accuracy as “functional performance” of SVD algorithm.

Taxotere effect on 18

FDG PET hyperintensities (SUV

values): Hyperintensities on MCF-7 induced rat tumor in vivo dynamic PET images are shown in Fig. (2). The locations of tumor were distinct and measurable with resolution of 0.5 mm, as shown in Fig. (2). After segmentation, control breast tumor showed distinct tumor boundaries. Different colors indicated the metabolic activity distribution at different tumor locations. In 24 hours post Texotere treated rats the tumor size was reduced in comparison with untreated tumor bearing control rats at same location as shown in Fig. (3) as extracted tumor areas. The distribution of metabolic activity at different tumor


location a – d was significant to correlate these malignant regions and MRI signal intensities as shown in Table 1. The distribution of both sodium MRI signal intensities and PET hyperintensities SUV values in different tumor locations was significant to correlate MRI-PET visible regions with premalignancy characteristics by histology shown in Table 2.

Taxotere effect on extracellular and intracellular

sodium image intensities: High MRI signal intensities are shown on MCF-7 induced rat tumor in vivo extracellular (EC) sodium and intracellular (IR) sodium images in Fig. (3). The sodium MRI signal intensities of tumors were comparable with sodium phantom images at optimized inversion time (TI = 25 ms) and echo time (TE = 10 ms). The IR-MRI images showed better contrast between breast tumor and normal rat tissue compared to the EC-MRI images on T1 weighted contrast scan settings as shown in Fig. (2).

The sodium MRI images of in vivo MCF-7 tumors are shown in Figs. (2, 3), at baseline, 24 hours and 48 hours after intravenous injection of 1 mg/ml in 0.25 ml of Taxotere in the femoral vein. Both EC-MRI and IR-MRI image showed contrast resolution similarity. However, contrast enhance-ment is shown as the IR-MRI line profiles following Taxotere chemotherapy in Fig. (3). The visible intracellular sodium[Na]i increase was distinct by IR-MRI imaging but less visible by EC-MRI imaging after Taxotere chemo-therapy. Using a standard high temperature sensitive super-conducting copper coil enhanced the signal-to-noise ratio of IR-MRI images by a factor of 10.5 times in less imaging time by a factor of 10 times.

Tumor histology evaluation criteria and MRI-PET

imaging comparison: The histology distinguished the tumor necrosis, apoptosis and different malignant tumor features

Fig. (2). A rat breast tumor after 21 weeks of propagation is shown in first row on top with excised histology slide (in middle) for registration

with mitotic figures, MCF-7 cell MALDI, tumor cytomorphometry, tunnel maps, DNA cycle M/S bars (first row on right A-D panels) and

MRI-PET images at different slice levels (second row on left). Notice the histology of tumor provides the ultimate cytomorphometric details

(different tumor i-m stages with tissue features) while same tissue locations show specific TOF-MALDI peaks and Monte-Carlos simulated

3D tumor reconstructed volume as display of 3D protein map in registration with MRI-PET piled up slice volume. The detailed protein

distribution (on bottom panel at left) with MS MALDI peaks (on bottom panel in the middle) provide peptide informatics or tumorigenic

protein 3D makeup (shown with tumor locations 1-5 with 4 at back side) in the tumor volume.


under high power field microscopy as shown in Fig. (2) in pre-treatment tumors. Different tumor features were measured of cyst size up to 40-200 microns, membrane blabbing, beaded nuclei 2-5 per cell and extracellular space up to 100-300 microns observed under high power field on slide and each division showed distinct MRI/PET signal intensities as shown in Table 1. The cyst showed low MRI signal intensities and extracellular space showed high MRI intensities. The PET showed high signal intensities in both cyst and extracellular space features. However, necrosis, cell proliferation regions showed isointense MRI images as shown in Fig. (2) coregistred with panels (i-m for different stages) on histology. The tumor evaluation ‘quantitative molecular imaging criterion’ suggested a simple, reproducible tumor grading scheme with minimum intra-operator bias to evaluate the tumor features and changes after anticancer taxotere drug effect on tumor chemosensitivity. The delineated areas on both histology digital images and MRI-PET fused images showed a comparable tumor morphometric measurement with r

2 = 0.997, P value = 0.002

as shown in Figs. (2, 3). However, histology method showed 20% less areas due to shrinkage of tumor tissues as shown in Figs. (2, 3).

Registration error and accuracy measurement: Rigid registration method showed two different points annotated as bright tumor regions and dark tumor regions as PET images. The brighter point set included all brighter nodes of the tumor from Moriginal. The dark point set included all dark-gray voxels of tumor from tumor from Moriginal. The RMSE accuracy was measured in millimeters for rigid registration method [1]. The tumor regions from Moriginalwere registered with static (Ptr and Pem) PET transmission and emission volumes.

Monitoring of therapeutic response by tumor

histology and signal intensity changes in IC Na MRIand

PET images: The effect of Taxotere after 12 hours was not visible by MRI and PET techniques. Taxotere enhanced the localized tumor signal intensities on intracellular sodium on MRI by 25% and glucose uptake on PET by 10% after 24 hours. However, later the effect was reduced after 48 hours as shown in Fig. (3). The post treated 48 hours end-point histology suggested the tumor cytomorphology features of enhanced apoptosis, cyst size and mitotic index comparable with enhanced IR sodium and PET signal intensities at these tumor locations. Apoptosis was evident by Annexin V immunostaining and cell cycle CAS 200 histograms showed M and S phases of neoplasia in selected histology matched tumor regions shown in Fig. (2). However, these less defined tumor tissue features were not feasible for histology and imaging co-registration.

MALDI imaging with MRI-PET and histology: The excised tumor specimens and their matched locations in MRI-PET showed a coregistered data set of ‘MALDI peak profile and histology cytomorphic features’ on small tissue sections are shown with i-m panels and MALDI peak spectra in Fig. (2) in high power microscopy fields. Same data is shown for registered MRI-PET signal intensities with MALDI array and registered histology details of each cytomorphic feature shown in Fig. (3) and Table 2. For achieving tumorigenic protein details from MALDI images, individual peaks A and B were characterized to sort out from

electrophoresis out of several spots of proteins as shown in Fig. (2).

Between the individual m/z values (between the peaks), no voltage change was observed because no ions arrive at the detector. The time between individual peaks was very much longer than the time taken by one peak width. The base line correction eliminated the noisy part of base line to record desired signal. Such processing was useful in identification of specific pattern of ion peak output voltage values for specific molecule in the sample. A total of 3 peaks (m/z with 6,630, 8,139 and 8,942 Da) were screened out by ‘support vector machine’ to construct the classification model with high discriminatory power in the training set as shown in Figs. (1, 2). The sensitivity and specificity of the model were 96.45 and 94.87%, respectively, in the blind-testing set. The candidate biomarker with m/z of 6,630 Da was found to be down-regulated in breast cancer tumors, and was identified as apolipoprotein C-I (unpublished data). Another two candidate biomarkers (8,139, 8,942 Da) were found up-regulated in breast cancer and identified as C-terminal-truncated form of C3a and complement component C3a, respectively (unpublished data). In addition, the level of apolipoprotein C-I progressively decreased with the clinical stages I, II, III and IV, and the expression of C-terminal-truncated form of C3a and complement component C3a gradually increased in tumor locations different from non-tumor locations (unpublished data). Other set of three MALDI peaks (m/z with 11250 Da (A), 13750 Da (B), and small peaks (C) at 13700 Da and 15200 Da positions) showed match with MRI-PET fusion signal intensities as shown in Figs. (3, 4).

MALDI Image Processing: Post-processed peaks and digitized ion images (m/z peak specific) for tumor volume reconstructions represented a set of peptide(s) and protein(s) indicated by respective spectral m/zpeaks as shown in Fig. (2). Monte-Carlos digitization performed well to generate point spread or point on MALDI-IMS image. Several cycles ofiterations (N = 10) further enhanced the sensitivity of protein or peptide specific peak selection and digitized peak point as simulated MALDI image as shown in Fig. (3).

MALDI IMS imaging: Integrating three-dimensional volume reconstructs of spatially resolved MALDI IMS ion images of whole breast tumor with fused high resolution MRI-PET images showed correlation between proteomic profiles with in vivo distribution of sodium and glucose uptake shown in Figs. (1-3). Each laser spot on MALDI plate corresponds to a pixel in a two-dimensional array of protein molecule or peptide make-up profile (proteomics content) of the selected point (on tumor slice on MALDI plate) predicting m/z peaks at every pixel. Three dimensional tissue volume reconstruct (display of simulated m/z data in 3 dimensions) by MALDI IMS construct provided the information of the MALDI IMS data or simulated 3D spatial finger print of proteomics relationship (protein pattern) with reconstructed 3D tumorigenic events in tumor 3D tissue volume. The MALDI-IMS information enhanced the protein-specific mass spectra as 3D anatomical distribution of protein annotation or distribution map for proteins (m/z) as visual ion volumes. Spatially resolved MALDI IMS data and volume rendered ion volumes fused with in vivo MRI-PET data suggested the possibility of breast tumor proteins


as associated with structural and functional events in the tumor cells (sodium symporter-glucose uptake status by MRI-PET) during tumorigenesis as drug evaluation testing method (see Figs. 2, 3).

The tumor areas with presence of tumor-specific proteins (apoptosis related high protein concentration or high ‘m/z’ ion volume on MALDI image) showed correlation with T1 contrast IC sodium variations in magnetic resonance and blockface volumes in the overlay renderings. In 24 hours post-texotere treated animals, IC sodium variation was

maximum without change in MALDI image proteomics profile. In selected voxels inside breast tumor and treated breast tumors showed protein peak (m/z) ion intensity values + 3 sd above mean value. Unpaired ‘f-test’and ‘t-test’ showed unequal variances of tumors (n = 16) vs normal tissue (n =3). Magnetic resonance images of rat breast tumor on a 4.23 Tesla clinical imager produced structural quantitative sodium parametric MRI images while PET generated physiological distribution of glucose uptake. Parametric comparison on MRI-PET images showed pixel intensities in both RGB colors and gray scale as biomarkers.

Fig. (3). (top panel) MRI-PET-MALDI data integration method is sketched. (bottom panel) A rat breast tumor (PET image on left) after

taxotere treatment is shown. First row (in middle at top) with MRI-PET image fusion at different MRI slice levels (second column in right).

Notice the high color coded signal intensities of tumor provides the taxotere effect while same tissue locations show specific TOF-MALDI

peaks as finger print of taxotere effect (second raw in middle) and Monte-Carlos simulated 3D tumor reconstructed volume as display of 3D

protein map in registration with MRI-PET piled up slice volume of shrunk tumor size (at bottom panel in middle). The detailed protein

distribution (on bottom panel at right) with MS-MALDI peaks provide peptide informatics or tumorigenic protein 3D makeup (peaks A and

B shown in tumor locations A-E) in the tumor volume.


The results indicated the correlation of ex vivo postmortem 3D proteomics and 2D histology correlation with in vivo 3D MRI-PET structural-physiological imaging. The ex vivo proteomics features were aligned and fused with corresponding ex vivo histology regions visible in the in vivo MRI-PET data at the resolution of 1.5 mm. The texotere

treated animal tumor cells showed distinct cytomorphic features on histology, distinct MALDI peaks, distinct MRI-PET signal intensities, distinct 3D MRI-PET-MALDI simulated construct display, different from normal tumor respective tumor features.

Fig. (4). (on top row) A tumor histology section with high power microscopy area (see arrows for apoptosis) is shown with corresponding

MALDI optimized peaks A and B. The peaks A and B were digitized by Monte Carlo simulation (A and B shown with arrows) and

integrated with MRI-PET images to generate a 3 dimensional tissue reconstruct. The tumor proteomics-image volume was used to compare

chemosensitivity.

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Statistical analysis of MALDI data showed spectra (in the range of 1-20 kDa) baseline subtracted and averaged peaks by centroid function gave peak resolution (FWHM) 1000-1500 (S/R = 8 for m/z 950 peak signals) on linear TOF mode setup as shown in Figs. (3, 4). Three MALDI peaks showed frequency graph of Gaussian distribution showed averaged distribution 0.83(95% confidence intervals 0.78-0.88) and peak intensities differed CV 20% with score between 0.7-1.0 by linear regression. It means lowest and highest relative intensities differ each other for < 30%. Cartesian plot analysis for influence of m/z over similarity score of each peak (within 0.7-1.0 range) showed reproducible data in tumors from rats as shown in Fig. (5).

DISCUSSION

The integrated MRI-PET-MALDI data fusion for evaluation of rat breast tumor is explored to identify apoptotic protein(s). Explanted MCF-7 cell lines are routine in experimental rat breast tumor propagation and testing anticancer drug chemosensitivity [27]. In vivo Taxotere treatment experimental doses used in rats were comparable to human clinical doses in previous study. Early tumor features and malignant lesions in rats induced with MCF-7 cell line showed biological, morphological similarities with many characteristics that closely mimic human breast cancer [1]. The proteomics information of MCF-7 cells is well documented by MALDI MS spectroscopy and PEG electrophoresis to identify potential protein biomarkers to predict response to chemotherapy in breast cancer [30-32]. Present study also indicated the possibility of protein

identification in tumor by MS peaks and presence of different proteins by PEG electrophoresis.

The selection of breast tumor areas with high signal intensity pseudo markers on MRI and PET images provided fine tumor alignment. However, better spatial resolution was still a challenge. The ‘convergence optimization’ of both MRI and PET common data points was reasonable to fuse them and approach was comparable with previous reports [21,23]. The SVD functional performance was comparable to calculate registration error with acceptable accuracy [33,34].

The distinct sodium MRI and glucose uptake PET signatures in tumor solid sites and semi-solid or fluid filled cyst regions in breast tumors were comparable with previous reports of different pre-malignant or malignant stages in tumor [1,14,20]. The tumor chemosensitivity to Taxotere was associated with solid tumor shrinkage or tumor cyst development showing up with low IR-MRI signal and enhanced EC-MRI signal on T1 weighted images. This observation corroborated with other reports indicating more loss of bound sodium in breast tumor after 48 hours than at 24 hours [1, 20].

The MRI/PET ‘similarity measure’ and validation by root mean square error (RMSE) was feasible for PET and MRI tumor size [35]. However, the PET method used an assumption that deformation to the intermediate tumor gray-dark point was proportional to the full deformation to the dark tumor regions in images. Poor MRI/PET similarity measurement accuracy artifacts were minimized by simulating MRI tumor size from segmented tumor size or

Table 1. Quantitative evaluation of MCF-7 rat breast tumor shows comparison of MRI-PET imaging with cytomorphic indices

and MALDI peaks to interpret the power of signal intensities before and after 24 hours taxotere treatment in mouse

tumor to represent chemosensitivity

SUV MALDI Histology KI-67 P.I. Pre-Drug treated@

and

Postdrug Treated# Tumor Features

Sodium MRI

Intensity

(kBq/ml)Peaks (in HPF) (A.I.) (in HPF) (Bars)

M/S-DNA

Tumor area (mm2; n=16)@ 4.40±0.3 - A,B 4.45±0.2 - - 3.7±0.2

Tumor area (mm2; n=3)# 4.35±0.2 - A,B 4.37±0.3 - - 3.2±0.4

IC/EC space@ 60-70 - A 65-75 - - -

IC/EC space# 84-95 - A 60-70 - - -

Necrosis* (squares)@ gray 84 A,B,C 45±25 - 260 M-DNA

Necrosis* (squares) bright 6350±21 - 125 M-DNA

Viable** (squares)@ dark 35 B or C 61±20 - - -

Viable** (squares)# dark 25 B or C 73±11 - - -

Apoptosis*** (nuclei)@ bright 48 A or B 44±12 160 - S-DNA

Apoptosis*** (nuclei)# bright 35 A or B 30±12 120 - M or S-DNA

Cyst**** (size in μm)@ gray 104 C 125±25 - - -

Cyst**** (size in μm)# gray 104 C 82±22 - - -

Pre-drug treated tumor (@) and post-treated tumor (#) by sodium MRI image intensity and histology. By usingeyepiece-micrometer square counter, necrosis*(<25% cells in micrometer

square), viable cells** (<60% cells inmicrometer square) and apoptosis*** (20-40 apoptotic nuclei in HPF) and cyst space****(< 100 μm) per HPF werepremalignant histology characteristics. IC/EC space (% space in HPF), necrosis, viable cells are shown as number ofmicrometer squares with <25% necrosis area in HPF by histology. Apoptotic index

(A.I.) and proliferation index (P.I.) are shown as average number of apoptotic nuclei per HPF and number of mitotic figures per HPF. S-DNAhistogram area was measured by CAS 200 system in arbitrary units. Single strand-DNA mAb area was measured indigital images by Optimas 6.5 and ss-DNA mAb density was measured in arbitrary units of

photomultiplier scanner.

The major three peaks were visible at 11250(A), 13750(B), and small peaks (C) at 13700 and 15200 positions (in m/z) on spectra as characteristics of rat breast tumor proteins shown in Figs. (3, 4).


computing NMI between static PET transmission volume (Ptr) and small MRI volumes chosen along the line of maximum 0.6 – 0.9 voxels away from zero translation in order to avoid segmentation errors as reported elsewhere [28]. The rigid registration of whole tumor borders in all directions also improves measurement accuracy [29]. Moreover, the deformations were restricted to anatomically tumor areas by locating the sphere centers on the triangle nodes.

The quantitative criterion served as numerical approach to measure tumor areas or size showing different tumor cytomorphic features, MALDI peaks and MRI/PET signal intensities at matched locations up to 100 micron resolution in pre-treatment or post-Taxotere treatment. Main features were: cyst hypointensity on MRI; necrosis with cell proliferation hyperintensity on PET, semisolid mass hypointensity on MRI and PET both modalities; Time of Flight-MALDI (TOF-MALDI) or Electrospray Ionization

(ESI) Mass Spectrometry of phosphopeptides from trypsin protein digests to have a large number of peaks; distinct cytomorphic features of apoptosis, necrosis, proliferation, cyst mass, aneucleosis by excised tumor histology. Earlier studies showed MRI-PET with histology as powerful tools for characterization and identification of phosphorylation sites [35]. Status of MALDI imaging as adjunct still remains disputed because of several artifacts including low intrinsic abundance, inefficient ionization, and/or signal suppression of most common peptides may limit or even prevent detection, unless the apoptosis sensitive phosphopeptide(s) content is significantly enriched by electrophoresis prior to MALDI analysis.

Similar to the present study showing peaks (m/z with 11250(A), 13750(B) Da major peaks and m/z with 13700 and 15200(C) Da small peaks shown in Fig. 3), three major peaks (m/z with 6,630, 8,139 and 8,942 Da) were earlier sorted out by ‘support vector machine’ to construct the

Table 2. A tumor histology evaluation criteria of comparing histology features with MRI/PET image signal intensities is

shown. The histology cytomorphic features of cystic fluid, apoptotic cells per high power field, extracellular volume, necrosis, viable cells are shown in mice prostate tumors. The degree of malignancy is shown with different extents as +

for mild, ++ for moderate, +++ for intense, ++++ for severe cell damage. Several malignancy features were

characteristic.

MALDI

m/z

Peaks*

Sodium MRI Signal

Intensity

IR Na MRI

PET

SUV

Histology

Tumor Features

Tumor Characteristics

Tumor Stage

B.C ++ +

+ +

Viable cells Active necrosis

Pre-Malignant Stage: Intraductal proliferation,

ductal hyperplacia, apoptosis

A,B,C ++++ +++ apoptosis

Malignant stage: Carcinoma (papillary; invasive comedo and cribriform)

sarcoma

dark/bright/gray + apoptosis

cyst, extracellular space neoplasia

*The major three peaks were visible at 11250(A), 13750(B), and small peaks at 13700 and 15200(C)positions (in m/z) on spectra as characteristics of rat breast tumor proteins in

tissue as shown in Fig. (3).

Fig. (5). The statistical analysis is for reproducibility of m/z data (frequency vs score) on left panel and linear regression analysis on right

panel.


confirmation and classification model with high discriminatory power in the training breast tumor data set [35]. Investigators reported primary invasive cancer proteins (C6, C11, C14, C16, and C26) different from five normal ones (N4, N15, N32, N33, and N36). Mass spectra on IMAC-Cu chip arrays using 1 μg of total protein showed protein expression profiles confirmed the breast cancer by two clusters: C6, C14, N32, N33, and N36 and the other C11, C16, C26, N4, and N15. A supervised cluster analysis by ProPeak (3Z Informatics, Charleston, SC) and biomarkers both separated the cancer data from noncancer data [35]. The sensitivity and specificity of the model were reported 96.45% and 94.87%, respectively, in the blind-testing set. The candidate biomarker with m/z of 6,630 Da was found as down-regulated in breast cancer patients, and was identified as apolipoprotein C-I. Other two candidate biomarkers (8,139, 8,942 Da) were found up-regulated in breast cancer and identified as C-terminal-truncated forms of C3a and complement component C3a, respectively. In addition, the level of apolipoprotein C-I progressively was decreased with the clinical stages I, II, III and IV, and the expression of C-terminal-truncated form of C3a and complement component C3a gradually increased in higher clinical stages [18, 35].

3D MALDI protein sorting and display by Monte Carlos

distribution as digital image of specific protein peak

(appeared as digital map similar with MRI-PET digital

images) was similar to previous study on 3D volume

reconstruction of proteins and peptides of breast cancer [19,

36]. Sinha et al. developed an iteration method to display

phosphopeptide Pea15 and Fabp5 proteins of glioma and

confirmed them by microscale affinity capture technique and

calibrated with standard phosphopeptides (phosphoserine,

phosphothreonine, and phosphotyrosine) [2]. Using same

approach in MCF-7 induced breast cancer model of explants

tumor we performed proteomics analysis to identify and

characterize tumor-associated protein variants associated

with apoptosis by two-dimensional electrophoresis (shown in

Fig. 1) and MALDI mass spectrometry. We characterized the

influence of N-methyl-N-nitrosourea (genotoxic nitroso

compound) as tumor-inducing agent on the protein pattern of

breast malignant tumor in rat. We found several tumor

apoptosis-associated variants AKR1C1 or -C3, AKR1B1

representing the proteins of the aldo-keto reductase

superfamily. We believed that apoptosis-associated protein

induction and/or protein inhibition were related to the

carcinogen MCF-7 bio-oxidation used for tumor induction.

The most prominent tumor-associated protein, rat aldose

reductase-like protein-1 (rARLP-1) (69% sequence identity

to lens aldose reductase) and three additional types of

rARLP-1 were detected in nitroso compound-induced rat

tumors, while rat aldo-keto reductase protein-c (Rak-c), a

novel tumor-associated variant (65% sequence identity with

3 -hydroxysteroid dehydrogenase) was identified in N-

methyl-N-nitrosourea-induced breast tumors (unpublished

data). Reduced 3 -hydroxysteroid dehydrogenase and 4 -3-

ketosteroid-5 -reductase enzymes both were tumor-specific

detoxification independent of MCF-7 induction. We believe

that MCF-7 carcinogen leaves a specific fingerprint(s) at the

proteomics level to manifest breast tumors. In contrast,

members of the aldo-keto reductase superfamily were not

reported as associated with MCF-7 induced changes in

proteomics peaks in breast tumor [37]. At this point much

remains to explore and investigate the apoptosis proteins in

breast tumor associated with induction or due to MCF-7

induction.

3D volume construction and 3D MALDI imaging coregistration with MRI/PET digital images was somewhat trivial because of MALDI sensitivity to protein mass; MRI sensitivity to protein bound sodium and PET sensitivity to radiolabel [18F] bound glucose. Moreover, apoptosis associated proteins and intracellular sodium bound proteins may or may not be dependent on glucose uptake (sodium symporter) or sodium pump and oxygen state in cell [38, 39]. Earlier reports suggested the active role of intracellular sodium and elevated glycolysis and reduced apoptosis in tumorigenesis and reversing or arresting or slowing down by anticancer drug chemosensitivity [40].

However, coregistration of 3D co-ordinates on MRI/PET and histology digital images was decisive and comparable with other report [41, 42]. The present study extended one more imaging MALDI modality to get composite information of tumor protein molecular details. The relationship of PET (18-FDG-glucose signal intensities) and MRI (sodium signal intensities)with MALDI (protein distribution at different tumor locations) and matched cytomorphological details (histology) may serve the purpose of cytomorphometrics and glycolytic tumor characterization with location of proteins (proteomics maps) to monitor real-time tumor chemosensitivity as a tool with possibility of in vivo diagnostic and therapeutics interpretation.

The real time monitoring of docetaxal (Taxotere) drug chemosenstivity effect during 0-48 hours was demonstrated in present study in terms of shrunken tumor mass by sodium MRI and decrease in hyperglycolytic tumor tissue with possible MALDI-IMS visible premalignancy malignancy specific tumor protein(s). However, the chemosensitivity effect was reduced at 48 hours end-point in comparison with chemosensitivity effect at 24 hours end-point. The tumor ex vivo cytomorphometry features added quantitative evaluation of drug chemosensitivity and supported to our previous observations [1, 20, 43].

However, there are several limitations of the use MRI/PET and predicting the tumor features. First, the tumor shrinkage during tissue processing of 4 micron tissue histology section limits the measurement of tumor histology area compared with 1000 micron MRI images. Second, 18-FDG PET images are dynamic and their projection images limit the correlation of glycolysis rich signals with sodium MRI signals. Third, different tumor features observed under high power fields may not always true representative of tumor staging as assumed in criteria. Fourth, identification of major carcinogenic responsible tumor proteins is a challenge because MALDI peaks are showing m/z peaks of proteins or peptides from a very small tumor region (difficult to take away specimen from big mass of tumor) while PEG electrophoresis protein/peptide map shows presence of tumor proteins (with different pI) in large number without any confirmation of responsible tumorgenic or apoptotic or premalignant protein(s). However, the intracellular sodium and glycolysis relationship stands valid in progressing tumor


cells as indicated earlier [43]. However, it remains to determine if tumor proteomics signatures have any impact on in vivo MRI-PET imaging signal contrast. Other important issue was how spatial and quantitative information from proteomics may extend the protein predictability and implications of in vivo MRI-PET image contrast distribution due to presence of odd numbered sodium nuclei and radio-labeled glucose. Proteomics automated image processing data [44] from in vivo MALDI-IMS studies may have potentials to test drug action or to predict functional regulatory protein information responsible of tumor apoptosis and angiogenesis (proteomics profiling), signaling mechanism and molecular mechanism of programmed tissue degradation (protein expression) and cancer protein mapping [45-50]. The spatial distribution of tumor cell protein molecules as false color maps can open window to the visible biochemical changes with insight of biophysical basis of MRI image contrast and physiological basis of PET contrast [51]. In future, technical advancements may accomplish the purpose of quantitative noninvasive MALDI imaging combined with multinuclear in vivo proton-intracellular sodium and glycolysis imaging indicators of tumorigenesis (apoptosis, necrosis, proliferation, premalig-nancy or malignancy) to test anticancer drug chemosensiti-vity [52]. It remains to see the new inventions how advanced techniques solve the problem of integrating in vivo imaging data with ex vivo molecular imaging data to construct three-dimension tumor volume of molecular details or IMAGINGTHERAPROTEOMICS to test anticancer drug effects.

CURRENT AND FUTURE DEVELOPMENTS

Recently several inventions and patents have suggested the possibility of multimodal imaging by integrating digital data from morphometric imaging with molecular imaging such as MALDI, immunostaining. The present review showed the distribution of 18-FDG-PET and sodium MRI signal intensities in tumor as measurable and diagnostic by imaging methods. Still there is no consensus on best configuration for PET/MRI system. There are three main approaches of PET/MRI integration architecture: sequential, insert and integrated types [53]. Major challenges are: 1. Potential cross talk effects in front-end electronics due to fluctuations in light yield of scientillators in PET detectors caused by rapidly changing MR gradients and RF signals; 2. Magnetic inhomogeneities; 3. Compensation of Eddy currents and better shimming; 4. Better PET attenuation-scatter-random coincidence correction algorithms; 5. Detector technology with matching scintillation crystals combined with less sensitive light sensors. In future new technology of magnetic field insensitive avalanche photodiodes, design shielded PET electronics will be available to avoid electromagnetic interference. In future, quantitative MRI-PET-MALDI-histoimmunostaining criterion can or will distinguish apoptosis-rich and benign or malignant tumor features for theradiagnostics. Sodium MRI and PET image intensities is a new information showing positive correlation with histology and apoptosis premalignancy proteomics indices as rapid drug monitoring time-dependent assay. In this direction, recently inventors modified and suggested design of transparent MALDI slides [54], antibody-peptide conjugate mediated MALDI imaging by fast fragmentation

method [55] and new thresholding techniques of MALDI peak selection. 3D digital mapping of MALDI is in infancy.

CONCLUSION

The physical basis of MALDI imaging and MRI-PET data integration is explored and patents are reviewed with a focus on the progress of quantitative MRI-PET and MALDI protein detection applications to test anticancer drug. Review of patents showed the approach of integrated MRI/PET imaging and immunostaining, histology and MALDI data may show correlation as sensitive, tumor specific, accurate reproducible and precise to define apoptosis in theradiagnostics of breast tumors in experimental rats.

ACKNOWLEDGEMENTS

This manuscript in part was presented at peer-reviewed AFLAC award at AACR meet 2002, ISMRM workshop 2001 and ISMRM annual meet 2002. MALDI-IMS data was presented by Doris Terry at ASMS 2007. The authors wish to acknowledge the experimental data and expertise provided by Drs. Ed X. Wu, Paul Cannon, van Heertum, Kenny Hess at Radiology department and Dr. Matthias Schbolcs and Dr. Mansukhani at Pathology department and helping in these imaging and continuing tumor histology experiments. Authors wish to acknowledge the MALDI-IMS and peak analysis done by Dr. Doris Terry at Florida State University, Tallahassee, Florida. Grant source: Aventis Pharmaceuticals Company, Bridge-water, NJ. Figures were improved by Mr. Magesh Sadasivam at Amity Institute of Nanotechnology, Amity University UP, NOIDA, India.

CONFLICT OF INTEREST

Authors do not have any financial or commercial conflict.

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Differentiating Proteomic Biomarkers in Breast Cancer by Laser

Capture Microdissection and MALDI MS

Melinda E. Sanders,*,†,O Eduardo C. Dias,‡ Baogang J. Xu,§,+ James A. Mobley,§,+

Dean Billheimer,| Heinrich Roder,⊥ Julia Grigorieva,⊥ Mitchell Dowsett,# Carlos L. Arteaga,‡,¶,O

and Richard M. Caprioli*,§,O,+

Departments of Pathology, Medicine, Biochemistry, Statistics, Cancer Biology, Breast Cancer Research Program,and the Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, Biodesix, Inc.,

Steamboat Springs, Colorado 80477, and Royal Marsden Hospital and Institute of Cancer Research,London, U.K.

Received December 03, 2007

We assessed proteomic patterns in breast cancer using MALDI MS and laser capture microdissectedcells. Protein and peptide expression in invasive mammary carcinoma versus normal mammaryepithelium and estrogen-receptor positive versus estrogen-receptor negative tumors were compared.Biomarker candidates were identified by statistical analysis and classifiers were developed and validatedin blinded test sets. Several of the m/z features used in the classifiers were identified by LC-MS/MSand two were confirmed by immunohistochemistry.

Keywords: MALDI MS • laser capture microdissection • breast cancer

Introduction

Breast cancer is the leading cause of cancer the USA forwomen and ranks second in cancer deaths, with an estimated182 460 new cases of invasive cancer, 67 770 cases of nonin-vasive breast cancer, and 40 480 deaths in 2008. Rather thanone disease, it is a heterogeneous group of neoplasms, someof which are locally aggressive and may metastasize early, whileother forms proliferate slowly and may be cured by surgicalexcision alone. Among the subset of “special type” carcinomas1

an excellent prognosis may be indicated by histology alone (e.g.,Pure tubular carcinoma); however, these represent less than15% of all breast cancers. The majority of “no special type”(aka. ductal carcinomas) have by definition no distinctivefeatures. Clinical decision making including the need forsystemic adjuvant therapy2–5 is currently based on a combina-tion of estrogen (ER) and progesterone (PR) receptor status andexpression levels, presence or absence of Her2-neu geneamplification, tumor size, grade, proliferative rate, and stage.6

Retrospective patient analyses including gene expressionprofiling suggest that differences in intrinsic biology of indi-vidual tumors have important implications for therapy andprognosis and that these differences are often not discernibleon a histological basis. Subsequent predictors of prognosis inbreast cancer based on cDNA expression7–10 have been devel-oped, some of which are in use in clinical trials.11–13 However,one would expect there ultimately to be limits to their predic-tive power because mRNA expression is poorly correlated withthe functional protein component. Accordingly, proteomicswhich studies the active mediators of cellular processes is arequired complement to gene expression analyses. Proteomicexpression among breast cancer subtypes is largely unexploredand should prove to be an important complement to microar-ray studies and an excellent mechanism for further under-standing these different phenotypes.

Matrix-assisted laser desorption/ionization mass spectrom-etry (MALDI MS) can profile proteins at high sensitivity up to50 kDa in tissues.14 This technology can directly measure manypeptides and proteins in tumor tissue sections and can alsobe used for high resolution imaging of individual biomoleculespresent in tissue sections.15–17 Coupled with laser capturemicrodissection (LCM), MALDI MS is an ideal approach forgeneration of separate protein profiles of the invasive tumorand normal epithelial components of breast tumors and tissues.In addition, epithelial elements usually compose only 5–15%of normal breast tissue making LCM mandatory in most casesto ensure a dominantly epithelial sample for evaluation. Weaimed to use MALDI MS to assess protein expression profilesin approximately 2000 cells from frozen sections of surgicallyresected breast tumors and reduction mammoplasty tissue, andto assess the resulting data using ProTS Marker software(Biodesix, Inc., Steamboat Springs, CO). The goal of this project

* To whom correspondence should be addressed. Melinda E. Sanders,M.D., Vanderbilt University Medical Center, 23rd and Pierce Ave., 4918-BTVC Bldg., Nashville, TN 37232. Phones, 615-322-1410(office), 615-343-9060(secretary); fax, 615-343-9563; e-mail, [email protected] M. Caprioli, Ph.D., Mass Spectrometry Research Center, 465 21stAvenue South, 9160 MRB-III, Vanderbilt University School of Medicine,Nashville, TN 37232, Phone, 615-322-4336, fax, 615-343-8372, e-mail,[email protected].

† Department of Pathology, Vanderbilt University.O Breast Cancer Research Program, Vanderbilt University.‡ Department ofMedicine, Vanderbilt University.§ Department of Biochemistry, Vanderbilt University.+ Mass Spectrometry Research Center, Vanderbilt University.| Department of Statistics, Vanderbilt University.⊥ Biodesix, Inc.# Royal Marsden Hospital and Institute of Cancer Research.¶ Department of Cancer Biology, Vanderbilt University.

1500 Journal of Proteome Research 2008, 7, 1500–1507 10.1021/pr7008109 CCC: $40.75 2008 American Chemical SocietyPublished on Web 04/04/2008

was to provide a distinctive protein profile of each tumor andassess the ability of our analysis algorithms to classify thetumors into ER-positive and ER-negative subgroups based ondifferences in these patterns. Recent results have shown thatMALDI MS-based diagnostics can be highly reproducible acrossdifferent laboratories, and may overcome some of the ambi-guities arising from other techniques.18

Experimental Procedures

Tissue Collection and Evaluation. A total of 122 invasivemammary carcinomas (IMC) and normal mammary epithelium(NME) from 167 reduction mammoplasty specimens wereanalyzed in this study. These samples were derived from 289women. These tissue samples were collected and distributedto our laboratory in a deidentified fashion by the four divisionsof the Cooperative Human Tissue Network and the RoyalMarsden Institute, U.K. (M.D.). Table 1. details the distributionof clinical and pathologic characteristics across centers. Noneof these women had received preoperative hormonal, chemo-,or radiation therapy. These tissues were obtained at the timeof the woman’s primary surgery, snap-frozen in liquid nitrogenwithin 30 min after removal from the patient, and stored at–80 °C until analyzed. The presence of tumor or NME wasconfirmed by a board-certified pathologist who specializes inbreast disease (M.E.S.) who examined a frozen section of eachtissue block and subsequently performed LCM on appropriateareas.

Tissue Sample Preparation. Sections for microdissectionwere prepared according to our previously developed proto-col.19 In brief, using a cryostat, 7 µm frozen tissue sections were

mounted on uncharged glass slides without the use of embed-ding media and placed immediately in 70% ethanol for 1 min.Subsequent dehydration was achieved using graded alcoholsand xylene treatments as follows: 95% ethanol for 30 s (2 times),100% ethanol for 30 s (2 times), and xylene for 5 min (2 times).Slides were then dried in a laminar flow hood for 10 min priorto microdissection.

Laser Capture Microdissection. LCM was performed usingthe PixCell IIe LCM system (Arcturus, Mountain View, CA).Depending on the size of the lesion, 500-1000 shots using the7.5 or 15 µm infrared laser beam were utilized to obtain anaverage of 2000 cells. All samples were microdissected induplicate.

Preparation of Microdissected Cells for MALDI MS. MALDIMS was performed directly on the LCM acquired cells. AfterLCM, the thermoplastic film was removed from the LCM capusing forceps and placed onto the MALDI plate using conduc-tive double-sided tape. A finely pulled glass capillary wasemployed to deposit as little as 10 nL of matrix solution asrequired to cover the captured cells under microscopic visu-alization. The matrix solution consisted of sinapinic acid at 20mg/mL in 6/4/0.01 (v/v/v) acetonitrile/water/TFA.

MALDI MS Analysis. MALDI MS analysis was performedusing a Voyager DE-STR MALDI time-of-flight mass spectrom-eter (Applied Biosystems, Framingham, MA) with a 337 nmnitrogen laser. Acquisition was achieved in the linear positiveion mode under optimized delayed extraction conditions asdescribed previously.14,15,20–24 Approximately 750 laser shotswere averaged to create a single spectrum from the capturedcells. In most cases, we generated three spectra per sample

Table 1. Clinical and Pathologic Characteristics of Samples Across Centersa

centers

1 2 3 4 5 Totals

IMC 24 9 13 3 73 122

Average age yrs (no.)b 66 (n ) 22) 57 (n ) 4) 65 (n ) 13) 49 (n ) 3) 53 (n ) 73)

GradeLow 7 1 0 0 8 16Intermediate 15 7 2 0 27 51High 2 1 11 3 38 55

Histologic typeNo Special type 23 6 13 3 70 115Special typec 1 3 0 0 3 7

Stageb

I 5 0 1 0 11 17II 6 0 4 0 19 31III 5 2 3 2 15 27

Hormone Receptor StatusER+/PR+ 10 6 5 0 20 41ER+/PR- 11 1 3 0 14 29ER-/PR+ 0 0 0 0 0 0ER-/PR- 3 2 5 3 34 47

NME 0 26 91 31 19 167Average age (yrs) N/A 36 33 33 31

a Centers: (1) Royal Marsden Hospital, U.K.; (2) CHTN Eastern Division, University of Pennsylvania, Philadelphia, PA; (3) CHTN Midwestern Division,Ohio State University; (4) CHTN Southern Division, University of Alabama, Birmingham, AL; (5) CHTN Western Division, Vanderbilt University.b Information was not available for all women. No reduction mammoplasty specimens were obtained from center 1. c Special type cancers: center 1,lobular-2; center 2, mucinous carcinoma-1, lobular carcinoma-2; center 5, tubular-1, mucinous carcinoma-2.

Biomarkers in Breast Cancer research articles

Journal of Proteome Research • Vol. 7, No. 4, 2008 1501

which were then combined to create one average spectrumwhich was used in the statistical analysis. In a subset of caseswhere the number of tumor cells was very small, we were ableto generate only one spectrum. In this analysis, signals in themass-to-charge (m/z) range from 2000 to 30 000 Da wereconsidered.

Immunohistochemistry. Estrogen receptor (ER) and proges-terone receptor (PR) expression were evaluated using theZymed PREDILUTED ER antibody 6F11 (S., San Francisco, CA)and the DAKO PgR636 PR antibody (Carpinteria, CA) each at a1:100 dilution and incubated for 1 h at room temperature.Immunohistochemistry was performed on frozen sections fromthe same tissue block on which LCM was performed. Theresults were scored as the percentage of tumor cells withnuclear staining. Tumors were considered to be ER-positiveor PR-positive when >10% nuclear staining was observed atany intensity.

Data Processing and Statistical Analysis. 1. Spectral Pre-processing. Preprocessing of spectra is necessary to renderspectra comparable and to ensure the reproducibility of thestatistical analysis procedure. Preprocessing was performedusing proprietary analysis tools developed by Biodesix, Inc.,CO. A detailed description can be found at http://www.biode-six.com in the technology section. Raw spectra were sent toBiodesix (Steamboat Springs, CO) for analysis. Mass spectragenerated over 2 years time, although run by the samepersonnel and on the same instrument, may exhibit variation.To enable analysis of these spectra, we applied a suite ofpreprocessing procedures25–29 and developed some additionalprocedures. In brief, the background and noise were estimatedand then subtracted from each spectrum based on local noiseestimators using a local (in m/z) robust asymmetric estimator25–29

and normalized to total ion current (TIC). Peaks were detectedusing a signal-to-noise ratio (S/N) cutoff of 4.0, which wasfound to be a good compromise between overdetection andsensitivity.

Spectra were aligned sequentially in three steps. The set ofcommon peaks appear in a plurality of spectra whose m/zvalues differ by < (5 Da was used for the alignment processusing a polynomial up to quadratic terms. The sets of pointsfor the alignment were selected based on the criteria ofcommunality (present in at least 2/3 of all spectra), S/N (S/N> 5 in the first alignment, S/N > 4 in the second and thirdalignment), and roughly even distribution along the m/z range.The following set of common peaks was used in the first phaseof the alignment process: m/z 3373.5, 4939.7, 5359.1, 5654.4,6548.4, 7670.7, 8570.2, 10091.7, 10839.3, 11309.6, 11349.3,11650.0, 12346.6, 13781.1, 14005.6, 15346.4, 15860.2, 17885.1,17926.1. Spectra were saved after the first alignment, reloaded,aligned again using a second set of peaks, and saved (m/z3449.0, 4939.8, 5360.7, 5653.9, 6175.2, 6546.9, 6650.0, 6890.2,7004.7, 8091.9, 9154.7, 10093.9, 10841.5, 11306.4, 113450.0,11653.78, 12347.9, 13780.2, 14009.9, 15342.6, 17893.3, 20945.4).Replicate spectra after the second alignment were used tocreate an average spectrum for each individual LCM sample.The third alignment was performed on the averaged spectrausing a third set of alignment peaks (m/z 4047.0, 4937.7, 5358.3,5652.8, 5939.5, 6175.8, 6277.6, 6547.7, 6665.1, 6890.6, 7005.8,8413.1, 8568.4, 9155.0, 9971.6, 10094.7, 10142.6, 10843.6, 11309.1,11653.4, 12349.0, 12652.4, 13780.1, 14010.0, 15341.1, 15867.9,17897.5, 20758.4, 26600.1). The preprocessing procedure wasoptimized using the training set and held fixed for the clas-sification of all test sets.

2. Feature Definition and Selection. Each MALDI spectrumis characterized by a set of features, which are defined asintegrated, background-subtracted, and normalized spectralintensities integrated over a chosen m/z range containing apeak. The m/z range for each feature was calculated from thealignment error and the local peak width of each spectrum.The features were predefined from a set of peaks that werecommon (within a predefined tolerance of 0.5 Da) to at least 3spectra of each clinical group. A combination of a selectedsubset of features and of the algorithm (and its parameters),which assigns a clinical label to a spectrum, constitutes aclassifier. Candidate features for the classification algorithmswere identified as differentially expressed m/z values fromspectra from IMC versus NME and ER-positive versus ER-negative tumors. The initial selection of significant features forthe classifier was based on a calculation of univariate p-valuesfrom Mann–Whitney U-tests (Wilcoxon rank sum test). Thena variant of a floating search method was applied.30 The floatingsearch looks iteratively at combinations of the significantfeatures to see how they perform as a classifier. As anoptimization criterion, we used the leave-one-out cross valida-tion (LOOCV) error on the training set. Finally, a visualinspection of features was carried out using the graphs of thegroup averaged spectra in the ProTS Marker software. In somecases, the feature widths were manually adjusted during thetraining process to take asymmetric peak shapes into account.

3. Classification Algorithm. The classification algorithmused was a straightforward implementation of a k-nearest-neighbor (KNN) algorithm.31 KNN requires as parameters a setof representative and labeled instances (i.e., list of selectedfeature values from a training sample set). Samples from eachclinical group of interest (IMC vs NME and ER-positive vs ER-negative tumors) were randomly split into training and test sets.Aligned average spectra were used for each sample instance,both in the training and in the test sets. To classify a newspectrum, the KNN algorithm first calculates the Euclideandistance of the feature values of the new spectrum to those ofthe training set spectra. This calculation yields a list of distancesfrom the test spectrum to each representative spectrum. Forthe k-nearest neighbors (those with the k smallest distances),the labels are compared. Finally, the assigned label is a simplemajority vote over the k-nearest neighbor labels. The number“k” of the nearest neighbors was the only classifier parameterused.

4. Cross-Validation of a Classifier and Validation. Cross-validation is an important tool in the assessment of classifierperformance during the training phase. A fixed number (onefor LOOCV or a prescribed number N for LNOCV) of instanceswas removed from the training set, a classifier was generatedusing the remaining instances, and the performance of thisclassifier was evaluated by applying it to the left-out instancesand comparing classifier and true labels. Ideally, this analysisis performed for all possible permutations of left-out and keptinstances in the training set. The average of the classificationperformance over these permutations is an estimate of theexpected performance of the classification. Classifier param-eters and the set of selected features were optimized using thesecross-validation procedures. After training and optimization,all parameters were frozen. No changes in the classificationalgorithm were allowed during the validation performed on theindependently and randomly selected test spectra.

5. Assignment of Training and Test Groups. Average spec-tra of IMC samples (122 instances) and NME samples (167

research articles Sanders et al.

1502 Journal of Proteome Research • Vol. 7, No. 4, 2008

instances) were split into test and training sets. A randomnumbers generator was used to assign each sample a number,then all samples were sorted by their numbers; the first 62 IMCinstances and 84 NME instances were assigned to the trainingset and used for generation of a classifier to distinguish IMCfrom NME. The remaining IMC and NME spectra were assignedto the test set. The same approach was used to randomly splitthe group of 117 IMC spectra with known ER status into ER+and ER- training and test sets. The training set consisted of36 ER+ and 25 ER- tumors, while the test set consisted of theremaining 32 ER+ and 24 ER- tumors.

Protein Identification. Once candidate molecular weightmarkers were selected by the class prediction model, we utilizeda combination of MS techniques previously validated by theVUMSRC15,32 to isolate and identify several of the proteinspecies of interest. The remainder of each specimen used forLCM was kept frozen to enable subsequent protein extractionand identification. Tissues with the highest relative intensityof the features of interest were selected for use in the proteinidentification. A total of 12 tissue fragments, 5 IMC and 7normal, were subsequently selected from this list because theMALDI spectra generated from these samples contained thelargest total number of the significant differentially expressedpeaks. Protein extracts were prepared using 3 to 4 mm3 portionsof mammary tissue in 1:20 (w/v) Tissue Protein ExtractionReagent (T-PER) plus Halt Protease Inhibitor Cocktail (10 µL/mL) (Pierce, Rockford, IL). Protein extracts were subjected toa cleanup step using a disposable hand-packed C18 preparativecolumn from Waters (Milford, MA). Samples were then frac-tionated with a Vydac (Hesperia, CA) 208TP5315 reversed-phaseC8 polymeric column at 40 °C using a Waters Alliance HPLCsystem (Milford, MA) using a flow rate of 0.5 mL/min. Thirtysecond fractions were collected into a 96 well plate using aGilson Fraction Collector (Middleton, WI) and further analyzedby MALDI MS and Flex Analysis software (Bruker-Daltonics,Billerica, MA) to determine the fractions containing the peaksof interest. The remaining volume of the fractions containingpeaks of interest was separated by one-dimensional gel elec-trophoresis. Bands were excised corresponding to the m/zpeaks of interest. Bands were in-gel-digested with trypsin, andsubjected to LC-MS/MS analysis on a Thermo LTQ linear iontrap instrument equipped with a Thermo nanoelectrospraysource, Surveyor LC system, and autosampler (Thermo Fisher,San Jose, CA). Tandem MS spectra were search against theUniRef human database using SEQUEST (Thermo Electron, SanJose, CA) and data filtered based upon the following filteringcriteria: cross correlation (Xcorr) value of >1.9 for singlycharged ions, > 2.2 for doubly charged ions, and >3.75 for triplycharged ions. A RSp (ranking of primary score) value of <4 anda dCN value of g0.1 were also required for positive peptideidentifications.33 A more detailed description of the proteinidentification methods is given in the Supporting Information.

Validation of Differentially Expressed Features. Two of thedifferentially expressed features were confirmed using twocommercially available tissue microarrays composed of paraf-fin-embedded tissue. The AcuMax A202IV array (ISU Abxis Co.)contained 45 breast cancers and 4 adjacent normal tissues induplicate cores. The US Biomax array consisted of 24 breasttumors with self-matching adjacent tissue and normal tissue(Ijamsville, MD), and was accompanied by ER, PR, and Her2/neu immunohistochemistry results and staging information.Each array was stained with the Sigma calcyclin/Anti-S100A6antibody (St. Louis, MO) at a 1:125 dilution for 1 h at room

temperature and the DAKO calgranulin A/clone MAC 387antibody (Carpinteria, CA) overnight at 4 °C. Antigen retrievalfor both antibodies was performed with proteinase K. Theresults were scored based on the intensity of cytoplasmicstaining on a scale of 0 to 3+ and percentage of cells stainingpositively. The Goodman Kruskal Γ was used as a measure ofassociation between the ordered categorical calcyclin andcalgranulin A staining and the clinicopathological variables. Forall tests, differences were considered significant for P-valuesless than 0.05. In this exploratory study, we seek to identifypotentially interesting relationships (with p < 0.05), rather thancontrol for experiment-wise error rate by using a reducedsignificance level for individual tests. Representative photomi-crographs showing staining with each antibody were taken withan Olympus DP-70 digital camera attached to an Olympus BX40microscope with a 22× ocular and a 20× lens.

Results and Discussion

Reproducibility and Influence of Clinical Covariates. Theintersample reproducibility of spectra is illustrated in Support-ing Information. Spectral replicas (after ProTS Marker prepro-cessing see above) were highly reproducible with a slightlyhigher variance of spectra in cancer samples than in normalsamples. We have also compared groups of samples originatingfrom different research centers using methods described aboveto evaluate possible differences attributable to specimenhandling at the different centers. In subsequent analyses, weensured that none of the features that had statistical signifi-cance in interinstitutional sample comparisons were used inthe classification of clinical groups. Details of this analysis arereported in the Supporting Information (Table 1). We per-formed similar comparisons of the NME from women 25-35years and greater than 45 years to examine the possibility thatage differences between samples may bias the classifier. Insubsequent analyses, we also ensured that none of the featuresthat had statistical significance in the age comparisons wereused in the classification of clinical groups. Details of thisanalysis are reported in the Supporting Information (Table 2).

Cancer versus Normal. To detect proteomic patterns inbreast tumors, we assessed the protein expression profiles of122 IMC and 167 examples of NME from reduction mammo-plasty specimens utilizing laser capture microdissection andMALDI MS. Spectra were obtained from an average of 2000cells dissected from frozen breast tissue by a breast pathologist(M.E.S.) using a serial hematoxylin and eosin stained sectionas a guide. Using wrapper methodology and cross-validationas a criterion, we created a classifier optimized for the correctclassification of the training set.

From 88 features considered, a set of 14 features was selectedto minimize the LOOCV error in the analysis of the trainingset comparing IMC and NME (Table 2). The details of thisfeature selection and the LOOCV error for various k-values arepresented in the Supporting Information (Tables 3–5). Usingthe class prediction model based on the selected signals and ak ) 7, we were able to distinguish IMC versus NME with 97%accuracy in the training cohort and 94% accuracy in the testingcohort with a sensitivity and specificity of 89% and 98%,respectively (Table 3). Discriminating features subsequentlyidentified by the protein identification studies are shown inFigure 1A. The complete set of discriminating features is shownin the Supporting Information (Table 2). While tumor can bedistinguished from normal mammary epithelium based onhistology alone, we believe that demonstrating a high accuracy



for a classifier distinguishing these groups is a necessary proofof concept. The next task is correlation of the proteomic profileswith known biomarkers such as estrogen receptor status.

ER Status. Sufficient residual tissue was available for 117tumors to evaluate ER status by immunohistochemistry (Table1). Of these, 61 tumors were ER+ and 56 tumors were ER-.On the basis of a training set of 36 ER+ tumors and 25 ER-tumors, a set of 4 features from among 94 features consideredwas selected to minimize the LOOCV error (Table 2). The detailsof this feature selection and the classification LOOCV error forvarious k-values are shown in the Supporting Information(Tables 6–8). Using the class prediction model based on theselected signals, a k-value of 5, and the remaining 32 ER+ and24 ER- tumors, we were able to distinguish ER+ and ER-tumors with 85% concordance with immunohistochemistry inthe training cohort and 66.1% accuracy in the testing cohortwith a sensitivity and specificity of 53% and 87.5%, respectively(Table 3). One discriminating feature subsequently identifiedby the protein identification studies is shown in Figure 1B.

To our knowledge, this is the first study to examine differ-ences in proteomic expression among ER+ and ER- tumorsutilizing MALDI MS of LCM acquired tumor cells. The studyof protein expression within these tumor subsets shouldprovide insights complementing those indicated by gene arraystudies because mRNA expression cannot always indicatewhich proteins are actually expressed and how their activitymight be modulated after translation.34,35 Therefore, analysisof the proteome directly from tumor tissue may provide a bettermolecular snapshot of the pathological status of the cancerthan gene expression patterns. As MALDI MS is an easy to use,reproducible, high-throughput technology, it may in the longrun provide a cheaper and faster alternative to genetic andimmunohistochemical approaches.

Classification accuracy for the ER+ and ER- tumors was lessaccurate than anticipated. There are likely several factorscontributing to this phenomenon. First, these subsets arethemselves heterogeneous groups as is well-documented bygene array studies.36 Second, we were limited by the smallnumber of samples available. Having 32 spectra in ER+ and24 spectra in ER- groups of the training cohort was apparentlynot enough to create a robust classifier. Thus, the results forER-status classification should be considered as preliminary.Still, identification of Calgranulin A (see below) as a significantdiscriminator indicates that even with a limited amount ofsamples we were able to obtain valuable information using ourapproach. We expect the classification to be noticeably im-proved and the protein identities of other significant featuresidentified once we have increased the sample size.

An advantage of our approach was the use of LCM-acquiredcells. The genomic studies classifying these tumor types usedwhole tumor tissues; thus, the presence of stroma, inflamma-tory cells, and small vessels also contribute to the tumorexpression profiles.

Protein Identification. Although the spectral profiles maybe useful for classification and prognosis, clues to the underly-ing biology of neoplastic transformation and progression canbe obtained from identification and functional investigationof these peptides and proteins. For protein identification, weprepared a tissue extract containing portions of 12 tissues (5IMC and 7 normal) remaining after LCM and MALDI analysisand then fractionated the proteins by HPLC. The tissues wereselected based on the fact that they had the highest relativeintensity of the features of interest. Monitoring of the fractionsby MALDI MS permitted identification of the fractions contain-ing the peaks of interest. Fractions of interest were then runon a trycine gel, and in-gel trypsin digests were performed onbands or molecular weight regions of interest. The resultingpeptide extracts were subjected to LC-MS/MS analysis. By thismethodology, we were able to identify 126 proteins by 2 ormore unique peptides; however, only 3 were among thestatistically significant discriminator peaks, ubiquitin m/z 8568,calcyclin (S100A6) 10094 m/z, and calgranulin A (S100A8) 10842m/z (Table 2).

Two of these classifiers, calcyclin and calgranulin A, wereconfirmed by immunohistochemistry using two different com-mercially available TMAs, an AccuMax array containing dupli-cate cores of 45 tumors which were accompanied by ER, PR,and Her2/neu staining results and a US Biomax TMA whichcontained 24 tumors and matched normal tissues. Figure 2shows the spectrum of staining intensity observed with bothantibodies. By our MALDI MS analysis, calcyclin expression wasgreater in normal compared to tumor tissues in the IMC versusNME comparison (Table 2). Correspondingly, on the USBiomax TMA, the number of normal tissues and the intensityof expression of calcyclin was significantly greater than intumor tissues (Table 4; p ) 0.02). Interestingly, in a subset ofcases, the myoepithelial component also stained positively forcalcyclin; however, this relationship did not reach statisticalsignificance. By our MALDI MS analysis, calgranulin A showedincreased expression in IMC relative to NME and served as aclassifier in the IMC versus NME comparison (Table 2), butwe found no statistically significant difference in the stainingintensities on the Biomax array (Table 4).

By our MALDI MS analysis, calgranulin A served as one ofthe features constituting the classifier for ER-negative tumors(Table 2). Calgranulin A staining is negatively associated withER status for tumors on the AccuMax array (Γ ) -0.53, p )0.007, see Table 4). Although not serving as a discriminator byour MALDI analysis, calcyclin expression is also negativelycorrelated with ER status (Γ) -0.67, p < 0.0001), which mightbe improved with performance of a true multivariate analysisas in the KNN classifier based on protein profiles.

The S100 Ca(2+)-binding proteins, a subfamily of EF-handCa(2+)-binding proteins, recently became of major interestbecause of their differential expression in neoplastic tissues,their involvement in metastatic processes, and the clusteredorganization of at least 10 S100 genes on human chromosome1q21, a region frequently rearranged in several tumors. Calcy-clin has implied roles in the regulation of cell growth anddivision, exhibits deregulated expression in association with celltransformation, and is found in high abundance in certain

Table 2. Features Used in Classifiersa

Classifier IMC vs NME

GreatestRelative

expression IMC NME

m/z 4205, 4938, 5421, 5827, 7176,8435, 8568, 10842, 11654

6891, 7651, 10094,13782, 22602

Classifier ER+ vs ER-

GreatestRelative

expression ER+ tumors ER- tumors

m/z 7177 6548, 9155, 10842

a The m/z values in boldface were subsequently identified as ubiquitin(m/z 8568), calcyclin (m/z 10094), and calgranulin A (m/z 10842).



breast cancer cell lines. In an immunohistochemical survey ofS100 protein expression in 28 tissue types and 21 tumor types,Cross et al. found expression of S100A6 and S100A8 in 12% and29% of breast cancers.37 Our findings are consistent with thoseof Carlsson et al. who found down-regulation of S100A6regardless of pathological stage and up-regulation of S100A8in breast cancer using serial analysis of gene expression(SAGE).38 Kennedy et al. have shown that BRCA1 is capable ofrepressing several of the members of the S100A family includingS100A8 and that functional BRCA1 is required for this repres-

sion.39 It is interesting that in our study the ER- and Her2-tumors showed the highest expression of S100A8 and these“triple negative” tumors are the subtype which contains tumorsfrom patients carrying BRCA-1 mutations.36,40 Mutant formsof BRCA-1 may be incapable of S100A8 repression. Finally,Ohuchida et al. have shown that inhibition of S100A6 decreasedproliferation and invasiveness of pancreatic cancer cell lines41

and Vimalachandran et al. demonstrated the high expressionof S100A6 (Calcyclin) is significantly associated with poorsurvival in pancreatic cancer patients.42 While we do not havespecific follow-up information on the patients in this study, itis striking that the tumor types with the highest expression ofcalcyclin in our study, triple negative and Her2-overexpressing,are known to have a poor prognosis.

Conclusions

The work reported here represents the first stage in theanalysis of proteomic expression in human breast tumorsutilizing MALDI MS and LCM-acquired cells from frozen tissuesections. Following spectral alignment and processing, biom-arker candidates were identified by statistical analysis. Wedeveloped classifiers for distinction of breast tumor versusnormal mammary epithelium and ER+ versus ER- tumorsusing test sets and then successfully used these classifiers inblinded test sets. Two of the m/z features, 10094 and 10842,were subsequently identified by LC-MS/MS as calcyclin(S100A6) and calgranulin A (S100A8) and confirmed by immu-

Figure 1. Representative features selected for classifiers. (A) Three representative features from the IMC vs NME classifier. The featuresm/z 8568, m/z 10094, and m/z 10842 in spectra generated from IMC are shown in blue and from NME in red. (B) A representativefeature for the ER-positive vs ER-negative classifier. The feature 10842 m/z from ER-positive tumors is shown in blue and the featurefor ER-negative tumors is shown in red. The spectra in the left column represent median spectra from the two groups. Bold linesrepresent the median spectrum for each group, and the thin lines represent 25th and 75th percentile. The spectra in the right columnshow the specific peak as it appears in each individual spectrum.

Table 3. Error Rates for the Classification of the IndependentTest Setsa

Classifier

test setIMC

(n ) 61)NME

(n ) 83)total

(n ) 144)

IMC vs NME Correct 54 81 135Error 7 2 9

Classifier

test setER+

(n ) 32)ER-

(n ) 24)total

(n ) 56)

ER+ vs ER- Correct 17 21 37Error 15 3 19

a Sensitivity, specificity, and accuracy for the IMC vs NME comparisonare 88.5%, 97.6%, and 93.7%, respectively. Sensitivity, specificity, andaccuracy for the ER+ vs ER- comparison are 53.0%, 87.5%, and 66.1%,respectively.



nohistochemistry. Additional studies to characterize the func-tion and biological role of these proteins in breast cancer willbe undertaken.

Proteomic expression in breast cancer was evaluated byMALDI MS of laser captured microdissected cells. Protein andpeptide expression in cancer versus normal mammary epithe-lium and estrogen receptor-positive versus estrogen receptor-negative tumors were compared. Biomarker candidates wereidentified by statistical analysis and classifiers developed usinga training set and validated in independent test sets. Severalm/z features used in the classifiers were identified by LC-MS/MS and two were confirmed by immunohistochemistry.

Acknowledgment. Dr. Sanders is the recipient of aKomen Foundation Award. Dr. Dias is the recipient of anAVON-AACR Scholarship Award. This project was supportedin part by The Vanderbilt Breast Cancer Specialized Programin Research Excellence (3P50 CA098131), NIH RO1 CA80195(C.L.A), and Cancer Center Support Grant P30 CA68485.

Supporting Information Available: (1) Detailed pro-tein identification methods, (2) reproducibility analyses, (3)interinstitutional sample variability, (4) variability of sampleswith age, (5) complete list of features defined for the IMC vsNME comparison, (6) subset of features used for the IMC vsNME classifier, (7) IMC vs NME classification LOOCV errordepending on selection of k-value for the KNN algorithm, (8)complete list of features defined for the ER+ vs ER- compari-son, (9) subset of features used for the ER+ vs ER- classifier,(10) ER+ vs ER- classification LOOCV error depending onselection of k-value for the KNN algorithm, (11) detailed proteinidentification data. This material is available free of charge viathe Internet at http://pubs.acs.org.

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Figure 2. Calcyclin and calgranulin A immunohistochemistry. (A)Calcyclin (upper panel). The photomicrographs show representa-tive examples of calyclin staining seen in tumor on the AccuMaxtissue microarray. The staining intensity ranged from 3+ (1), 2+(2), 1+ (3), to 0 (4). All photos were shot with a 22× ocular and20× lens with an Olympus DP-70 digital camera. (B) CalgranulinA (lower panel). The photomicrographs show representativeexamples of calgranulin A staining seen in tumor on the Accu-Max tissue microarray. The staining intensity ranged from 3+(1), 2+ (2), 1+ (3), to 0 (4). All photos are shot with a 22× ocularand 20× lens with an Olympus DP-70 digital camera.

Table 4. US Immunohistochemistry Staining Results forTissue Microarrays

US Biomax TMA Breast Cancer Cases vs Normal Breast

antigeninterpretable

cores (%)invasive

tumor (%)luminal

epithelium myoepithelium

Calcyclin+ 37 (77) 11 14 5Calcyclin- 8 4 13

Total 19 18 18P-value 0.02a 0.20a

Calgranulin+ 36 (75) 7 5 0Calgranulin- 12 12 0

Total 19 17 0P-value 0.51a 0.06a

AccuMax TMA Breast Cancer Cases and Tumor Subsets

antigeninterpretable

cores (%)positivestaining ER+ ER- Her2+ Her2-

Calcyclin+ 41 (91) 32 22 10 11 21Calcyclin- 8 8 0 4 4

Total 40 30 10 15 25P-value <0.0001b <0.14b

Calgranulin+ 41 (91) 25 16 9 10 15Calgranulin- 16 14 2 5 11

Total 41 30 11 15 26P-value 0.007b 0.51b

a Values are calculated with respect to invasive tumor. b Hypothesistests are computed based on the IHC scoring from 0 to 3+, but arecollapsed to positive and negative staining for this table.



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