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Cancer

Serum Peptidome Patterns That Distinguish Metastatic Thyroid Carcinoma from Cancer-free Controls Are Unbiased by Gender and Age^*,S

Josep Villanueva^,, Andrew J. Martorella^¶, Kevin Lawlor, John Philip, Martin Fleisher^||, Richard J. Robbins^¶,**, and Paul Tempst^,,

From the Protein Center, Molecular Biology Program, and Departments of ^¶ Medicine and ^|| Clinical Laboratories, Memorial Sloan-Kettering Cancer Center, New York, New York 10021 and ^** Department of Medicine, The Methodist Hospital, Houston, Texas 77030

	ABSTRACT

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Serum peptidomics is a special form of functional proteomics. The small number of blood proteins that are the source of most prominent peptides in human serum serve as a substrate pool for commonly occurring and/or cancer-derived proteases. Exoprotease activities in particular, when superimposed on the ex vivo coagulation and complement degradation pathways, contribute to generation of not only cancer-specific but also "cancer type"-specific serum peptides. Following development of a unique, semiautomated serum peptide profiling platform and after completing investigations to eliminate common experimental bias, we have now studied possible effects of gender and age on serum peptidomes of 200 healthy men and women, ages 20–80, and of 60 patients (30 men and 30 women) with metastatic thyroid carcinomas. Extensive MALDI-TOF MS and data analysis suggested negligible contributions of both age and gender to the serum peptidome patterns except that healthy men and women under 35 years, but not older individuals, could be distinguished with 70% accuracy. Considering the more advanced age of most patients, this finding is unlikely to interfere with peptidomics analysis of most cancers. By examining patient samples and age/gender-matched controls followed by variability analysis of either demographic or disease (versus control) groups, we could conclusively rule out demographic bias. An optimized, 12-peptide ion thyroid cancer signature was then developed, enabling classification of an independent validation set with 95% sensitivity and 95% specificity (binomial confidence intervals, 75.1–99.9%). Ten of these peptides had previously been assigned to signature patterns of other solid tumor cancers. One of the two newly discovered peptides was dehydro-Ala³-fibrinopeptide A. As we expand this study to include hundreds of thyroid cancer patients, the peptide signature will be adjusted, further validated, and then evaluated in a clinical setting used either independently or in combination with existing markers.

By correlating identified proteolytic patterns with several disease groups and controls, it has also been shown that exoprotease activities, superimposed on the ex vivo coagulation and complement degradation pathways, contribute to generation of not only cancer-specific but also "cancer type"-specific serum peptides (9). None of the signature peptides appear to be derived from cancer tissues, implying that different tumor types secrete and/or shed distinct proteases that through their catalytic activity may generate unique serum peptide profiles. The small number of blood proteins that were the source of nearly all the peptides in the prostate, bladder, and breast cancer signatures are therefore not bona fide biomarkers but appear to serve as an endogenous substrate pool for tumor-derived proteases. There was also no relationship between the precursor substrate concentrations and the MS ion intensities of many of the degradation products. For instance, highly abundant serum proteins such as albumin and immunoglobulins were not represented. It therefore appeared that a direct link exists between peptide marker profiles of disease and differential protease activities and that the patterns may have clinical utility as surrogate markers for detection and classification of cancer.

Although critics of serum peptidomics have often pointed at demographics as a potential source of bias, to our knowledge this has never been systematically investigated. We therefore wanted to address whether parameters such as gender and age influence peptidome profiles as obtained using our mass spectrometry-based serum peptide profiling platform. To this end, we collected blood samples from healthy individuals of both genders between the ages of 20 and 80 years and analyzed them using our serum peptidomics methodology. Furthermore this study was specifically intended to verify whether age and gender effects can mask disease effects in serum peptidomics analyses and/or whether differences in peptidome profiles thought to be associated with a specific disease may instead represent demographic bias. The study group was therefore designed to comprise equal numbers of men and women and equal numbers of cancer patients and age-matched healthy individuals. Thyroid cancer, the most common endocrine malignancy, was chosen for this investigation as it is the focus of a major proteomics initiative at our institution.

Since 1973, the incidence of thyroid cancer has increased nearly 50%, and the American Cancer Society projects 30,000 new cases in 2006. There are over 300,000 thyroid cancer survivors in the United States who are under surveillance. Currently there is not a single test that is perfectly capable to predict the presence or absence of thyroid cancer before surgery. The current tumor marker, thyroglobulin, is secreted by all normal thyroid follicular cells and well differentiated thyroid carcinomas. Several thyroglobulin assays are available, but there are several assay limitations that reduce the diagnostic accuracy in predicting residual cancer (10). It has great utility in monitoring patients who have had their entire thyroid removed but makes no distinction between the presence of benign or malignant tissue prior to or after surgery. Patients must therefore undergo fine needle aspiration of any suspicious thyroid nodules and several diagnostic modalities such as ultrasound, radioiodine scanning, and positron emission tomography/computed tomography scanning to determine the existence of metastatic disease. Serum peptidome profiling has the potential to replace presurgical diagnostic modalities as the gold standard for use in thyroid cancer diagnosis and follow-up surveillance testing. It also has the potential to predict which patients harbor residual or recurrent disease and may provide a likelihood prediction of which lesions respond best to radioactive iodine, chemotherapy, or external beam radiotherapy.

Here we report that extensive MALDI-TOF MS and data analysis of serum peptidomes of 200 healthy men and women and of 60 metastatic thyroid carcinoma patients indicated only negligible contributions of both age and gender to the patterns except that healthy men and women under 35 years, but not older individuals, could be distinguished with better than chance accuracy. Through variability analysis of either demographic or disease (versus control) groups, we ruled out demographic bias. A 12-member peptide ion thyroid cancer signature was developed and enabled classification of an independent validation set with 95% sensitivity and 95% specificity. One of the signature peptides was dehydro-Ala³-fibrinopeptide A. As we expand our studies to include hundreds of thyroid cancer patients, the peptide signature will likely be adjusted, further validated, and evaluated in a clinical setting.

	EXPERIMENTAL PROCEDURES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Materials
Acetonitrile was obtained from Burdick and Jackson (Muskegon, WI), TFA was from Pierce, and premade -cyano-4-hydroxycinnamic acid matrix solution was from Agilent (Palo Alto, CA). Human serum (catalog number S-7023, lot 034K8937) was obtained from Sigma. Serum peptide processing was done in 0.2-ml polypropylene tubes (8 x 12-tube TempPlate II) from USA Scientific (Ocala, FL).

Serum Samples
Blood samples from volunteer subjects with no known malignancies (Supplemental Table 1) and from patients diagnosed with metastatic thyroid carcinoma were collected following our standard clinical protocol (8) and after obtaining informed consent. Details on patient age, gender, and pathologic diagnosis are given in Supplemental Table 2. All collections were approved by the Memorial Sloan-Kettering Cancer Center Institutional Review and Privacy Board. Blood samples were collected in 8.5-ml, BD Vacutainer SST "tiger-top" tubes (BD Biosciences catalog number 367988), allowed to clot at room temperature for 1 h, and centrifuged at 1400–2000 relative centrifugal force for 10 min at room temperature (8). Sera (upper phase) were transferred to four 4-ml cryovials (Fisher catalog number 0566966), with 1 ml serum in each, and stored frozen at –80 °C until further use. Upon arrival at the MS laboratory, the cryovials (source vials) were barcoded using the Memorial Sloan-Kettering Cancer Center Clinical Proteomics laboratory information management system (see below) and a Z4M barcode printer (Zebra Technologies, Vernon Hills, IL) (8). One cryovial of each sample was thawed on ice and used to generate nine smaller aliquots (50 µl each) in micro-Eppendorf tubes that were also barcoded and stored at –80 °C in barcoded freezer boxes until further use. Each serum sample had therefore been frozen and thawed twice before it was subjected to solid-phase peptide extraction and MS.

Automated, Solid-phase Peptide Extraction
Serum peptide profiling was done as described previously (7, 8). Peptides were captured and concentrated using SiMAG-C8/K superparamagnetic, silica-based particles bearing C₈ reversed-phase ligands (Chemicell, Berlin, Germany). All analyses were performed in a 96-well format using the same batch of C₈ magnetic particles, in 0.2-ml polypropylene tubes, using a Genesis Freedom 100 (Tecan) liquid handling work station. This system automates all liquid handling steps, including magnetic separation via a robotic manipulating arm, mixing of eluates with MALDI matrix, and deposition onto the Bruker 384-spot MALDI target plates. A computer randomization program is used to position case and control samples for both solid-phase extraction and mass spectrometry.

Mass Spectrometry
Peptide profiles were analyzed with an Autoflex MALDI-TOF mass spectrometer (Bruker, Billerica, MA) as described previously (7). Separate spectra were obtained for two restricted m/z ranges, corresponding to polypeptides with molecular mass of 0.7–4 kDa ("4 kDa") and 4–15 kDa ("4 kDa") (assuming z = 1) under specifically optimized instrument settings. Each spectrum was the result of 400 laser shots, per m/z segment per sample, delivered in four sets of 100 shots (at 50-Hz frequency) to each of four different locations on the surface of the spot. The irradiation program was automated using the "AutoXecute" function of the instrument. Spectra were acquired in linear mode geometry under 20 kV (18.6 kV during delayed extraction) of ion accelerating and –1.3 kV multiplier potentials and with gating of mass ions 400 m/z (4-kDa segment) or 3,000 m/z (4-kDa segment). Delayed extraction was maintained for 80 (4 kDa) or 50 ns (4 kDa) to give time lag focusing after each laser shot. Peptide samples were always mixed with 2 volumes of premade -cyano-4-hydroxycinnamic acid matrix solution (Agilent), deposited onto the stainless steel target surface in every other column of the 384-spot layout, and allowed to dry at room temperature. A weekly performance test using commercial human reference serum (Sigma catalog number S-7023, lot 034K8937) was done, and the effective laser energy delivered to the target was adjusted as necessary (8).

Assigning Peptide Sequences
Peptides selected on the basis of statistical differences in ion intensity between cancer and control groups were analyzed by MALDI-TOF/TOF tandem mass spectrometry using an UltraFlex TOF/TOF instrument (Bruker) operated in "LIFT" mode. The monoisotopic masses were first assigned by one-dimensional reflectron-TOF MS in the presence of three peptide calibrants as described previously (9). Spectra were obtained by averaging multiple signals; laser irradiance and number of acquisitions (typically 100–150) were operator-adjusted to yield maximal peak deflections derived from the digitizer in real time. Monoisotopic masses were assigned for all selected and other prominent peaks after visual inspection, and the low and high end internal standards were used for recalibration. The pass/fail criterion for recalibration is a correct assignment of an m/z value for the "middle" calibrant with a mass accuracy equal or better than 12 ppm.

Alternatively a QSTAR XL Hybrid quadrupole (Q) time-of-flight mass spectrometer (Applied Biosystems/MDS Sciex) equipped with an o-MALDI ion source was used for both duplicate and additional tandem MS analyses. By selecting precursor ions of interest in "Q1" (operated in the mass filter mode), mass measurements of fragment ions could be obtained in the TOF detector following CID in "Q2." Typically a mass window of 3 Da was selected to transmit the entire isotopic envelope of the precursor ion species. Collision energy was operator-adjusted to yield maximum number and intensities of the fragment ions.

Fragment ion spectra resulting from TOF/TOF and Q-TOF analyses (300–1,000 acquisitions averaged per spectrum) were taken to search a "non-redundant" human database (NCBInr; release date, May 20, 2005; 134,668 entries; National Center for Biotechnology Information, Bethesda, MD) using the MASCOT MS/MS ion search program, version 2.0.04 for Windows (Matrix Science Ltd., London, UK) with the following search parameters: monoisotopic precursor mass tolerance of 40 ppm, fragment mass tolerance of 0.5 Da, and without a specified protease cleavage site. Mascot "mowse" scores greater than 35 were considered significant. Any identification thus obtained was verified independently by two different people by comparing the computer-generated fragment ion series of the predicted peptide with the experimental MS/MS data. Some sequence identifications had below threshold scores but could nonetheless be unequivocally assigned as the precursor ion mass and selected fragment ion masses (b'' or y'') matched a particular peptide in a nested set of sequences (9) (Supplemental Table 3), taking also into account that the limited fragmentation patterns were in agreement with established rules of preferential peptide bond cleavage (e.g. Pro-directed fragmentation).

Signal Processing
The custom signal processing software used in this study has been extensively described in an earlier report (8). Data are stored with a naming convention that allows each sample to be associated with its calibrant. The spectra are then converted from binary format to ASCII files and processed in MATLAB with a custom script, "Qcealign," that uses the "Qpeaks" program (Spectrum Square Associates, Ithaca, NY) for smoothing, base-line subtraction, and peak labeling. The singletwidth parameter was set to –400 for the lower mass range and –200 for the upper mass range, thereby specifying the resolution, (m/z)/(m/z), for processing. Peak information was used automatically by Qpeaks in setting the parameters for smoothing, base-line subtraction, and binning. The noise statistics were assumed to be "Normal."

Following parameter selection, a setup file was created, and Qcealign queried this file to obtain a list of directories for processing. All data files in all listed directories were aligned with each other during a single processing run. For each directory, singletwidth information was provided in the setup file along with parameters controlling calibration, peak labeling sensitivity, alignment, etc. The files containing the polypeptide standards were first calibrated, the centroid positions of peaks were then obtained from the peak table and compared with the known polypeptide peak positions, and a quadratic calibration equation for correcting the measured masses in each calibration file was created. Qcealign then creates a reference file to which all sample spectra will later be aligned, then loads all other sample files, calibrates them, and adds the intensities to the intensity of the reference file to create the average of all the sample files. The base line was subtracted and normalized to unit size by dividing each intensity value by the total ion count, and a scaling factor was added by multiplying each intensity value by a user-selected number (e.g. 10⁷). A peak table, smoothed curve, and a base line were then created, and the spectrum was taken for alignment. A custom alignment algorithm, "Entropycal," was then used to align sample data files to a reference file using a minimum entropy algorithm by taking unsmoothed ("raw"), base line-corrected data (11). The alignment was performed in two steps. At each relative position n, the Shannon entropy of the sum of the two files was computed, and the optimal alignment occurred at the shift that produces the lower value. The smoothed spectrum was then updated to reflect the aligned m/z values, and the peak table was updated. The peak lists were then binned by using the resolution of the peaks. All peaks in rows within (m/z) of the strongest peak at a given value of m/z are binned together, and a spreadsheet was created for further statistical analysis.

MATLAB Software Tools
Three software modules, developed in MATLAB, were used for visualization and signal processing of the spectra (8). (i) Signal Processing & Preview (SPP), a graphical viewer for spectra in ASCII format, allows plotting raw and processed spectra side-by-side to review the outcome of signal processing. Furthermore parameters of Qpeaks (the signal processing software) can be adjusted. (ii) Mass Spectra Viewer (MSV),¹ a visual interface for processed spectral data, plots spectra as x-y curves (mass versus magnitude) for examining the signatures of several groups of samples. MSV supports regular browsing functions such as scroll, zoom, highlighting, etc. (iii) HeatMap (HM) displays spectra as two-dimensional heat map images in which the magnitude of the peaks are color-coded on a continuous scale. In addition to browsing functions such as zoom and scroll, the rank of x- and y-position coordinates can be reorganized without the constraints of statistical correlation that are enforced by most HeatMap commercial software packages.

Statistical Analysis
The spreadsheet ("peak list"; see "Signal Processing"), containing "binned" data from spectra obtained for all samples of cancer patients and healthy subjects (260 samples total; 598 m/z values with normalized intensities for each sample; >155,000 data points), were imported into the GeneSpring program (Agilent). Different "experiments" were created in GeneSpring to represent the masses. No normalizations were applied to the experiment because the masses were normalized by the database that binned them. In the "Experiment’s Interpretation" section, the Analysis mode was set to "Ratio" (signal/control), and all measurements were used. No Cross-Gene Error model was used.

Class Comparison—
The 598 m/z values were subjected to average-linkage hierarchical clustering using standard correlation (also known as "Pearson correlation around zero") as a distance metric (GeneSpring program). Peaks were organized by creating mock phylogenetic trees ("dendrograms"). Trees were then displayed with the samples along the x axis and the masses along the y axis. Principal component analysis (PCA) on samples was also done using the GeneSpring program.

Feature Selection—
Once the experiments were created, the m/z values ("peaks") were filtered by using non-parametric tests: Mann-Whitney U test (for binary comparisons) and Kruskal-Wallis test (for multiclass comparisons) on each of the 598 features. Significance levels were adjusted to account for the consequences of multiple testing using the method of Benjamini and Hochberg (12) that limits the false discovery rate (FDR). These tests are meant to find peaks that show statistically significant differences between the clinical groups studied.

Class Prediction—
Support vector machine (SVM) and K-nearest neighbor (K-NN) analyses were done using the Class Prediction Tool in GeneSpring. Leave-one-out cross-validation (LOOCV) experiments were done using both SVM and K-NN modeling on all 598 peptides. In K-NN modeling the number of neighbors was set to 3, 5, 7, or 10 with a p value decision cutoff of 1. The kernels used in SVM modeling were: linear, polynomial (order 2), polynomial (order 3), and radial. The class prediction strategy to optimize the thyroid cancer peptide signature was done as follows. Several models using K-NN and SVM algorithms were built using the classification error rate in the cross-validation of the training set (n = 80) as the criterion for parameter optimization. In these analyses, different combinations of peptides selected by the Mann-Whitney U test at different adjusted p value cutoffs were used to build the models. The best models (the ones giving the smaller classification error rate in the cross-validation of the training set) were then tested in the validation set (n = 40). In addition to different combinations of peptides selected using the Mann-Whitney U test, random combinations of peptides were tested using our class prediction models with the validation set. Five different random combinations of 17, 12, and five features were generated out of the 598 features and tested with the validation set. Their classifications rates were averaged (five different experiments) and compared with the classifications rates of the true combinations of features obtained during feature selection.

	RESULTS

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Age- and Gender-associated Serum Peptidome Variability Is Minimal—
To evaluate the possible effects of major demographic parameters such as gender and age on serum peptide profiling, we collected blood samples from 200 healthy volunteers and prepared the corresponding sera using a single, standard clinical protocol. Volunteers were recruited for this study to result in three, roughly equal size age groups: 20–35 years old (37 women and 33 men), 35–50 years old (33 women and 34 men), and over 50 years old (39 women and 24 men) (see Figs. 1 and 2, left panel). Gender and precise age of each volunteer are listed in Supplemental Table 1. Postcollection sample handling was uniform, involving two freeze-thaw cycles to accomplish initial storage and subsequent aliquoting for peptide extraction and MS analysis (8). All samples were processed fully automatically (i.e. peptides were extracted on magnetic beads coated with C₈ phase, washed, eluted, mixed with matrix, and deposited on the MALDI target plate), as a single batch, and using a customized robot liquid handler followed within 1 h by automated MALDI-TOF mass spectrometric analysis (see "Experimental Procedures"). System reproducibility was always first verified on the same day by analysis, computer alignment, and visual comparison of 12 reference serum samples/spectra as described previously (8). Samples were randomly distributed during processing and analysis. Processed spectra (see "Experimental Procedures") were aligned using the custom Entropycal algorithm (8), and a total of 598 distinct m/z values, or peaks, were resolved in the 700–8,000-Da range. A spreadsheet (peak list), containing the normalized intensities (i.e. signal intensities, after base-line subtraction, were divided by the total ion current of the corresponding spectrum and multiplied by a scaling factor of 1 x 10⁷), of all 598 peaks for each of the 200 samples was then taken for unsupervised analysis.

View larger version (31K): [in this window] [in a new window]   FIG. 1. Study overview. The diagram shows (i) the design of the serum peptidome demographic study and (ii) the approach used for development and independent validation of the 17-peptide ion thyroid cancer signature. Numbers that are encircled indicate total number of selected peptides at that stage of the study. Thyroid MET, patients with metastatic thyroid carcinomas; F, female; M, male.

View larger version (43K): [in this window] [in a new window]   FIG. 2. Age distribution of healthy controls and thyroid carcinoma patients in this study. Colored fields indicate the three age groups used throughout this study (see also Fig. 1). Black horizontal lines represent medians for each separate cohort.

View larger version (58K): [in this window] [in a new window]   FIG. 3. Unsupervised hierarchical clustering and principal component analysis of mass spectrometry-based serum peptide profiling data derived from three age groups of healthy men and women. Serum samples were prepared following the standard protocol. The samples were randomized before automated solid-phase peptide extraction and MALDI-TOF mass spectrometry. Spectra were processed and aligned using the Qcealign script. A peak list containing normalized intensities of 598 m/z values for each of the 200 samples was generated. Dendrograms (left panels) represent samples and have been generated by unsupervised, average-linkage hierarchical clustering using standard correlation. The entire peak list (598 x 200) was used. Dendrogram colors follow the age and gender color-coding schemes as indicated. Peptide clusters are not shown. PCA of the same 200 samples is shown in the right panels. The first three principal components, accounting for most of the variance in the original dataset, are shown.

Cancer Is a Major Source of Variability in Serum Peptidome Profiles—
As part of an ongoing, related project at our host institution, we have also collected and analyzed sera from 60 patients who had clear evidence for residual metastatic thyroid carcinoma (30 men and 30 women aged between 15 and 86) (Figs. 1 and 2, right panel). Specimens are linked to database records (listed in Supplemental Table 2) but were anonymized and stripped of any patient identifiers to meet Health Insurance Portability and Accountability Act guidelines. Blood collection, sera preparation, storage and handling, automated peptide solid-phase extraction, and mass spectrometry were done exactly as for the 200 controls. In fact, all 260 serum samples were analyzed simultaneously but in a positionally randomized manner (i.e. positioning in the microtiter plate wells and on the MALDI target plates was determined by a computer randomization program).

When the 260 peptidome profiles were analyzed by average-linkage hierarchical clustering using all 598 peptides, two roughly separated clusters emerged. The bigger cluster (Fig. 4A, left side of the dendrogram) contained 191 of the 200 healthy volunteer samples (colored in red), whereas 56 of the 60 thyroid cancer patient samples (colored in blue) were contained in the cluster on the right. This observation is in clear contrast with the scattering of gender and age groups in the dendrograms shown in Fig. 3. PCA revealed a similar grouping with the healthy controls and the metastatic cancers in largely separated clusters (Fig. 4B). Taken together, the clusters in Figs. 3 and 4 suggest that thyroid cancer (i.e. patients harboring metastatic lesions) is by far the major source of variance in our 260-sample serum peptidome dataset, resulting in near completely separable groups except for four outlying patient samples (573a, 655a, 758a, and 764a are highlighted in Supplemental Table 2) and nine outlying controls (four males, 23, 24, 33, and 35 years old, and five females, 27, 50, 51, 51, and 57 years old; highlighted in Supplemental Table 1). We don not have a ready explanation for the outliers. In a third and final analysis, we examined the variance (analysis of variance) for each of the 598 peptides for each of the three study cohort parameters. This test calculates the association strength between the peptidome profiles and the parameters under study (see "Experimental Procedures"). A Mann-Whitney U test (to compare both genders or to compare healthy controls versus disease) or Kruskal-Wallis test (to compare the three age categories) was performed, and a p value was calculated for each peptide in each comparison. Using a p < 0.05 cutoff, 110, six, and three differential peptides were retrieved from the (i) cancer versus control, (ii) gender, and (iii) age group comparisons, respectively; the smallest p value obtained for each these comparisons was, respectively, 9.97 x 10^–36, 3.99 x 10^–5, and 1.81 x 10^–4. The negative logs of all p values were then summed to create a measure termed "association strength" (i.e. when smaller p values are generated for more significant parameters, association strength will increase), which was 3,137, 779, and 638, respectively. Overall our analyses revealed that a much larger number of discriminating peptides more strongly associated with disease than with any particular age group or gender.

View larger version (32K): [in this window] [in a new window]   FIG. 4. Unsupervised hierarchical clustering and principal component analysis of MS-based serum peptide profiling data derived from healthy controls and thyroid carcinoma patients. Samples were prepared and analyzed, and the peak lists were generated and subjected to clustering analysis and PCA as described in Fig. 3. Color-coding of 200 controls and 60 cancer patients is as indicated in the inset.

View larger version (18K): [in this window] [in a new window]   FIG. 5. Age distribution of individuals in the training and validation sets used in this study. Equal numbers of men and women are included in each of the four subgroups (see also in Fig. 1). Black horizontal lines represent medians for each subgroup.

View larger version (84K): [in this window] [in a new window]   FIG. 6. Unsupervised hierarchical clustering and principal component analysis of MS-based serum peptide profiling data derived from healthy controls and thyroid carcinoma patients. Samples were prepared and analyzed, and the peak lists were generated and subjected to clustering analysis (left side) and PCA (right side) as described in Fig. 3. The entire peak list (598 x 80) was used. Columns represent samples; rows are m/z peaks (i.e. peptides). The heat map scale of normalized ion intensities is shown. Color-coding of the dendrograms and PCA plots of the 40 controls and 40 thyroid cancer patients (panels in A) and of the 40 men and 40 women (panels in B) are as indicated.

View larger version (60K): [in this window] [in a new window]   FIG. 7. MALDI-TOF mass spectral overlays of the 17 most discriminating peaks derived from serum peptide profiling of thyroid cancer patients and healthy controls. Spectra were obtained, aligned, and normalized as described under "Experimental Procedures" and are displayed using the MSV. Peptide ions represent the "17-peptide thyroid cancer signature" as described in the text and also shown in Fig. 8. Each of the overlays (not to scale) contains 80 spectra with normalized intensities: 40 controls (in red) and 40 thyroid cancer patients (blue). The monoisotopic mass (m/z) is shown for each peptide ion peak.

View larger version (49K): [in this window] [in a new window]   FIG. 8. Feature selection and comparative analysis of serum peptide profiling data derived from thyroid cancer patients and healthy controls. The peak list (598 x 80) was subjected to a Mann-Whitney U test for each individual cancer versus the control. Only peaks with adjusted p values <0.00001 were passed through a second filter (median peak intensity, >500 "units"); a peak was selected if it passed the intensity threshold in either cancer or control. The heat map compares the 17 selected features (the thyroid cancer signature) for each sample in the study set. Columns represent samples (per group); rows are m/z peaks as indicated by the monoisotopic mass (on the left) and peptide sequence (on the right) for each. Serum peptide sequences highlighted in pink are newly identified in this study. The longest peptide (i.e. ending in ***) is shortened as listed here; the complete sequence (54 amino acids) is SSSYSKQFTSSTSYNRGDSTFESKSYKMADEAGSEADHEGTHSTKRGHAKSRPV. Thyroid MET, patients with metastatic thyroid carcinomas.

Fifteen of the 17 selected serum peptides (Fig. 8) that constitute the metastatic thyroid cancer signature had already been identified by MALDI-TOF/TOF and MALDI-Q-TOF MS/MS analysis and database searches in an earlier study (9). As reported, most of these previously identified peptides clustered into nested sets of overlapping sequences. Likewise 15 peptides from the current study collapsed into three sequence clusters (Fig. 8 and sequences on a blue field in Supplemental Table 3). Two clusters are derived from naturally occurring serum peptides, fibrinopeptide A (FPA) and complement C3f. The third cluster maps to a different region of fibrinogen-. Some sequence assignments (Supplemental Fig. 1) had well below threshold scores (see "Experimental Procedures") but could nonetheless be unequivocally assigned as the precursor ion mass, and selected fragment ion masses (b" or y") matched a particular rung in the ladder, taking also into account that the limited fragmentation patterns were in agreement with established rules of preferential peptide bond cleavage and the putative sequence. The two "unknown" peptides (m/z = 1,519 and 5,902) of the 17-peptide signature were also analyzed by TOF/TOF MS/MS (Supplemental Fig. 1) and here identified for the first time as dehydroalanine-containing FPA and a 54-amino acid-long fibrinogen- fragment (shown on a pink field in Fig. 8), respectively. Both peptides gave consistently lower MS ion signals in the cancer patient sera than in the matched controls (Figs. 7 and 8). Dehydro-Ala was unequivocally mapped to position 3 in the sequence of the "1,519" peptide by comparison with MS/MS spectra of unmodified FPA (Supplemental Fig. 1 and data not shown) and is most likely derived by ß-elimination from a phospho-Ser residue (13) known to naturally occur at that position in a subset of FPA and fibrinogen molecules (14–16).

Peptide Ion Signatures Provide Accurate Class Prediction for a Validation Set of Thyroid Carcinoma and Control Samples—
Robustness of the thyroid cancer peptide signatures was tested in an independent validation set of serum samples collected from 20 patients with thyroid cancer (10 men and 10 women) and 20 age-matched controls (10 men and 10 women); see Fig. 5, right panel. None of the samples in the validation set had been included in the earlier supervised analysis, therefore allowing for the estimation of true predictive accuracy. Either all 598 peptide ions or the 17-peptide signature was used for comparison of four separate groups (Fig. 9A: cancer/training, blue; control/training, red; cancer/validation, yellow; control/validation, light blue) by PCA (Fig. 7B). In this unsupervised analysis, samples from thyroid cancer sets 1 and 2 were relatively well separated from the control sets 1 and 2.

View larger version (26K): [in this window] [in a new window]   FIG. 9. Independent set of thyroid cancer and control serum samples for validation of the "17-peptide biomarker signature." A, study design. See Fig. 1 and "Results." B, principal component analysis of the four independent thyroid cancer 1 and 2 groups and control 1 and 2 groups based on either all 598 peptide ions (left panel) or the 17-peptide ion signature only (i.e. the other 581 peptides were ignored; right panel). The first three principal components, accounting for most of the variance in the original dataset, are shown. M, male; F, female.

View larger version (15K): [in this window] [in a new window]   FIG. 10. Sensitivity and specificity of serum peptide signatures to classify thyroid cancer and healthy controls in an independent validation set. Models were built using K-NN machine learning, and the classification error rate in leave-one-out cross-validation of the training set (40 patients and 40 matched controls) was used for parameter optimization. The best models were then tested for prediction accuracy on a completely independent validation set (20 patients and 20 matched controls). Numbers of peptides in the signatures were first selected by a Mann-Whitney U test of the training set using progressively smaller FDR-adjusted p value cutoffs (i.e. no selection, 10–5, 10–7, and 10–10) and ranged from 598 (all) to five. K-NN-based class prediction of the validation was done using three, five, seven, or ten neighbors. Random combinations of 17, 12, and five peptides were generated five times each, and the classification error rates were averaged and then also tested for class prediction accuracy of the validation set using the same models.

	DISCUSSION

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

We have developed and applied a unique serum profiling platform (7) to study a large cohort of identically collected and processed samples from 200 healthy men and women, ages 20–80, and from 60 patients (30 men and 30 women) with metastatic thyroid carcinoma. Extensive MALDI-TOF MS and data analysis suggested only negligible contributions of both age and gender to the serum peptidome pattern. Feature selection and statistical analysis further indicated that possible distinguishing characteristics were generally no better than what could be expected by chance. For instance, leave-one-out class predictions for gender or age group comparisons gave around 40–60% accuracy with one minor exception. In the age group under 35 years, but not in any other age brackets, healthy men and women could be distinguished with 70% accuracy by LOOCV based on the serum peptide patterns. Although this is by no means an accurate classification rate, it is likely better than chance in a study cohort of 70 individuals (33 men and 37 women; 50 correctly classified by LOOCV). We can only speculate at this time what the underlying reason(s) might be for this observation. All the same, the effect is rather subtle and, considering the more advanced age of most cancer patients, unlikely to interfere with peptidomics analysis of cancer sera.

We nonetheless verified whether differences in serum peptidome profiles thought to be associated with cancer could instead represent demographic bias. A study group was therefore assembled to comprise equal numbers of men and women and equal numbers of metastatic thyroid cancer patients and age-matched healthy controls. Unsupervised hierarchical clustering resulted in a fairly clear-cut separation of cancer and controls, whereas gender distribution was basically random. In addition, a much larger number of discriminating peptides more strongly associated with disease than with any particular age group or gender. Using statistical and other filtering methods and a class prediction optimization strategy based on LOOCV and machine learning methods, a 12-peptide thyroid cancer signature was then obtained and enabled classification of a totally independent validation set with 95% sensitivity and 95% specificity. Interestingly 10 of these peptides had previously been assigned to signature patterns of other solid tumors (e.g. breast, prostate, and bladder cancer) (9). Unfortunately the use of different blood collection tubes in the current study and in those earlier studies precluded meaningful comparisons or multiclass predictions of the thyroid cancer signature.

One of the two unique, newly identified peptides in the thyroid cancer signature is dehydro-Ala-FPA, which is most likely derived by ß-elimination from the phospho-Ser residue (13) known to naturally occur at that position in about 20–30% of FPA and fibrinogen molecules (14–16). Curiously we have never observed phospho-FPA in either cancer patient or control samples, a fact that may actually be related to poor MALDI in positive mode (29). Therefore, dehydro-Ala-FPA could simply be a surrogate for phospho-FPA in MALDI-TOF MS-based serum profiling. We do not know at this point whether the conversion occurred in serum or during sample processing or mass spectrometry (30). It is puzzling, however, why ß-elimination, generally thought to be induced by alkaline conditions (13), could have occurred at the low pH that is maintained throughout our serum processing protocol. It is also of note that phospho-Ser³-FPA has previously been detected at elevated levels in sera of ovarian cancer patients (31). By contrast, we found dehydro-Ala-FPA at significantly lower concentrations in thyroid cancer patient sera relative to the controls.

In summary, the serum peptidome patterns that distinguish metastatic thyroid carcinoma from cancer-free controls are unbiased by gender and age and appear to have diagnostic potential with high sensitivity and specificity to classify samples in an independent validation set. We believe as we expand the current study to include hundreds more thyroid cancer patients that the peptide signature can be appropriately adjusted, optimized, and further validated. It will then be evaluated in a clinical setting either used independently or in combination with existing diagnostic procedures such as fine needle aspiration biopsy or serum thyroglobulin measurements.

Furthermore our approach can be generalized for many diagnostic and predictive purposes, as an in vitro phenotypic readout of catalytic and metabolic activities in body fluids or tissues, utilizing either endogenous substrates or measured quantities of externally added, isotopically labeled substrate analogs followed by quantitative product analysis. Various analytical readouts, product selection schemes, and activity attenuation procedures can be envisioned to provide more, or different, data points and to tailor the process to each specific case of pattern discovery. Alternatively identification and characterization of the protease panels may lead to direct immunoassay-based, quantitative diagnostic tests suitable for a clinical environment.

	ACKNOWLEDGMENTS

 
We thank all volunteers for generous donation of blood samples as well as Dr. Lilian Reich and the staff members of Memorial Sloan-Kettering Cancer Center’s blood bank for enthusiastic assistance. We are indebted to Dr. Hediye Erdjument-Bromage for expert help with TOF/TOF MS/MS-based peptide sequencing.

	FOOTNOTES

 
Received, June 20, 2006, and in revised form, August 8, 2006.

Published, MCP Papers in Press, August 8, 2006, DOI 10.1074/mcp.M600229-MCP200

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

¹ The abbreviations used are: MSV, Mass Spectra Viewer; FDR, false discovery rate; FPA, fibrinopeptide A; K-NN, K-nearest neighbor; LOOCV, leave-one-out cross-validation; PCA, principal component analysis; SVM, support vector machine.

^* This work was supported by National Institutes of Health Grants 1 R21 CA1119425, 5 P30 CA08748, and 5 P50 CA92629; the Himanshu Vakil Research Fund; and the Byrne Fund.

^S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

To whom correspondence may be addressed: Memorial Sloan-Kettering Cancer Center, 1275 York Ave., New York, NY 10021. E-mail: rjrobbins{at}tmh.tmc.edu

To whom correspondence may be addressed: Memorial Sloan-Kettering Cancer Center, 1275 York Ave., New York, NY 10021. E-mail: p-tempst{at}mskcc.org

	REFERENCES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 

1. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., and Liotta, L. A. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572 –577[CrossRef][Medline]

2. Adam, B. L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z., and Wright, G. L., Jr. (2002) Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 62, 3609 –3614[Abstract/Free Full Text]

3. Li, J., Zhang, Z., Rosenzweig, J., Wang, Y. Y., and Chan, D. W. (2002) Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin. Chem. 48, 1296 –1304[Abstract/Free Full Text]

4. Conrads, T. P., Fusaro, V. A., Ross, S., Johann, D., Rajapakse, V., Hitt, B. A., Steinberg, S. M., Kohn, E. C., Fishman, D. A., Whitely, G., Barrett, J. C., Liotta, L. A., Petricoin, E. F., III, and Veenstra, T. D. (2004) High-resolution serum proteomic features for ovarian cancer detection. Endocr. Relat. Cancer 11, 163 –178[Abstract]

5. Coombes, K. R., Morris, J. S., Hu, J., Edmonson, S. R., and Baggerly, K. A. (2005) Serum proteomics profiling-a young technology begins to mature. Nat. Biotechnol. 23, 291 –292[CrossRef][Medline]

6. Check, E. (2004) Proteomics and cancer: running before we can walk? Nature 429, 496 –497[CrossRef][Medline]

7. Villanueva, J., Philip, J., Entenberg, D., Chaparro, C. A., Tanwar, M. K., Holland, E. C., and Tempst, P. (2004) Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. Anal. Chem. 76, 1560 –1570[Medline]

8. Villanueva, J., Philip, J., Chaparro, C. A., Li, Y., Toledo-Crow, R., DeNoyer, L., Fleisher, M., Robbins, R. J., and Tempst, P. (2005) Correcting com-mon errors in identifying cancer-specific serum peptide signatures. J. Proteome Res. 4, 1060 –1072[CrossRef][Medline]

9. Villanueva, J., Shaffer, D. R., Philip, J., Chaparro, C. A., Erdjument-Bromage, H., Olshen, A. B., Fleisher, M., Lilja, H., Brogi, E., Boyd, J., Sanchez-Carbayo, M., Holland, E. C., Cordon-Cardo, C., Scher, H. I., and Tempst, P. (2006) Differential exoprotease activities confer tumor-specific serum peptidome patterns. J. Clin. Investig. 116, 271 –284[CrossRef][Medline]

10. Robbins, R. J., Srivastava, S., Shaha, A., Ghossein, R., Larson, S. M., Fleisher, M., and Tuttle, R. M. (2004) Factors influencing the basal and recombinant human thyrotropin-stimulated serum thyroglobulin in patients with metastatic thyroid carcinoma. J. Clin. Endocrinol. Metab. 89, 6010 –6016[Abstract/Free Full Text]

11. Shannon, C. E. (1948) A mathematical theory of communication. Bell Syst. Tech. J. 27, 379 –423

12. Benjamini, Y., and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289 –300

13. McLachlin, D. T., and Chait, B. T. (2003) Improved ß-elimination-based affinity purification strategy for enrichment of phosphopeptides. Anal. Chem. 75, 6826 –6836[Medline]

14. Blombaeck, B., Blombaeck, M., Edman, P., and Hessel, B. (1962) Amino-acid sequence and the occurrence of phosphorus in human fibrinopeptides. Nature 193, 833 –834[CrossRef][Medline]

15. Binnie, C. G., Hettasch, J. M., Strickland, E., and Lord, S. T. (1993) Characterization of purified recombinant fibrinogen: partial phosphorylation of fibrinopeptide A. Biochemistry 32, 107 –113[CrossRef][Medline]

16. Maurer, M. C., Peng, J. L., An, S. S., Trosset, J. Y., Henschen-Edman, A., and Scheraga, H. A. (1998) Structural examination of the influence of phosphorylation on the binding of fibrinopeptide A to bovine thrombin. Biochemistry 37, 5888 –5902[CrossRef][Medline]

17. Morley, M., Molony, C. M., Weber, T. M., Devlin, J. L., Ewens, K. G., Spielman, R. S., and Cheung, V. G. (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743 –747[CrossRef][Medline]

18. Oleksiak, M. F., Churchill, G. A., and Crawford, D. L. (2002) Variation in gene expression within and among natural populations. Nat. Genet. 32, 261 –266[CrossRef][Medline]

19. Jin, W., Riley, R. M., Wolfinger, R. D., White, K. P., Passador-Gurgel, G., and Gibson, G. (2001) The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat. Genet. 29, 389 –395[CrossRef][Medline]

20. Enard, W., Khaitovich, P., Klose, J., Zollner, S., Heissig, F., Giavalisco, P., Nieselt-Struwe, K., Muchmore, E., Varki, A., Ravid, R., Doxiadis, G. M., Bontrop, R. E., and Paabo, S. (2002) Intra- and interspecific variation in primate gene expression patterns. Science 296, 340 –343[Abstract/Free Full Text]

21. Pritchard, C. C., Hsu, L., Delrow, J., and Nelson, P. S. (2001) Project normal: defining normal variance in mouse gene expression. Proc. Natl. Acad. Sci. U. S. A. 98, 13266 –13271[Abstract/Free Full Text]

22. Cheung, V. G., Conlin, L. K., Weber, T. M., Arcaro, M., Jen, K. Y., Morley, M., and Spielman, R. S. (2003) Natural variation in human gene expression assessed in lymphoblastoid cells. Nat. Genet. 33, 422 –425[CrossRef][Medline]

23. Whitney, A. R., Diehn, M., Popper, S. J., Alizadeh, A. A., Boldrick, J. C., Relman, D. A., and Brown, P. O. (2003) Individuality and variation in gene expression patterns in human blood. Proc. Natl. Acad. Sci. U. S. A. 100, 1896 –1901[Abstract/Free Full Text]

24. Jiang, M., Ryu, J., Kiraly, M., Duke, K., Reinke, V., and Kim, S. K. (2001) Genome-wide analysis of developmental and sex-regulated gene expression profiles in Caenorhabditis elegans. Proc. Natl. Acad. Sci. U. S. A. 98, 218 –223[Abstract/Free Full Text]

25. Jiang, C. H., Tsien, J. Z., Schultz, P. G., and Hu, Y. (2001) The effects of aging on gene expression in the hypothalamus and cortex of mice. Proc. Natl. Acad. Sci. U. S. A. 98, 1930 –1934[Abstract/Free Full Text]

26. Roth, S. M., Ferrell, R. E., Peters, D. G., Metter, E. J., Hurley, B. F., and Rogers, M. A. (2002) Influence of age, sex, and strength training on human muscle gene expression determined by microarray. Physiol. Genomics 10, 181 –190[Abstract/Free Full Text]

27. Johnson, F. B., Sinclair, D. A., and Guarente, L. (1999) Molecular biology of aging. Cell 96, 291 –302[CrossRef][Medline]

28. Kujoth, G. C., Hiona, A., Pugh, T. D., Someya, S., Panzer, K., Wohlgemuth, S. E., Hofer, T., Seo, A. Y., Sullivan, R., Jobling, W. A., Morrow, J. D.