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Cell and Tumor Biology |
Departments of 1 Biochemistry, 2 Neurosurgery, 3 Biostatistics, 4 Molecular Physiology and Biophysics, and 5 Pathology and 6 Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee; 7 Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, NIH, Bethesda, Maryland; and 8 Brain Tumor Institute, Cleveland Clinic Foundation, Cleveland, Ohio
Requests for reprints: Richard M. Caprioli, Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue South, Room 9160, Medical Research Building 3, Nashville, TN 37232-8575. Phone: 615-322-4336; Fax: 615-343-8372; E-mail: r.caprioli{at}vanderbilt.edu.
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Abstract |
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Introduction |
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25,000 new cases per year. Half of these tumors exhibit aggressive, infiltrative behavior, respond poorly to treatment, and are characterized as one of the more fatal human malignancies. Clinical diagnosis and treatment decisions for these tumors are based almost exclusively on tissue histology. Accurate glioma classification is therefore critical. Histologic diagnosis predominantly uses the WHO classification scheme (1), which classifies gliomas based on the principal cell type and assigns them a grade of I to IV by increasing degrees of malignancy. Current methods for diagnosing gliomas, however, are highly subjective and depend on the skill of the neuropathologist. Considerable diagnostic variability also occurs because of the heterogeneous and highly infiltrative nature of these tumors. Moreover, no reliable molecular markers are known to exist that accurately categorize tumors in a clinically relevant fashion. There is therefore a critical need to develop more precise, nonsubjective, and systematic methods to classify human gliomas based on molecular markers. Recent advances have used cDNA microarrays to identify tumor grade-specific gene patterns (2–8) and potential markers of survival (9–11). In addition, two-dimensional gel electrophoresis technology has been used to analyze known tumor biomarkers for grade-specific trends (12) and monitor protein changes relative to tumor grade and patient survival (13). Most of these approaches rely on histopathologic analysis for accuracy verification and are low throughput, and some require a foreknowledge of the markers of interest. Few efforts have focused on developing a grade-independent, high-throughput, patient prognostic tool applicable in a clinical setting.
We have developed a direct-tissue protein profiling approach to tumor analysis using matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) to correlate protein patterns obtained directly from tumor biopsies with patient survival trends. This technology is characterized by high mass measurement accuracy (100-200 ppm; ref. 14), high sensitivity (attomoles to femtomoles), and a potential for high throughput (prepared samples can be analyzed in <5 minutes). The mass spectra acquired from tissue biopsies reflect a portion of the protein content within the tissue (i.e., 300-500 proteins). This technology has been used previously for imaging protein localization within a tumor biopsy (15), monitoring protein changes in mouse prostate (16) and rat pituitary and pancreas (17), and identifying tumor and prognostic specific biomarkers for patients with lung carcinomas (18).
We report the identification of MALDI MS prognostic-specific protein patterns based on the analysis of 162 tissue biopsies from 127 patients. Protein patterns were identified that accurately classify glioma subtypes and distinguish patients into two prognostic groups, a short-term survival (STS) group and a long-term survival (LTS) group. In addition, we studied a well-characterized subset of patients with grade IV gliomas and identified protein patterns that predict differential survival.
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Materials and Methods |
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Collecting and processing clinical material and patient information. Tissues were obtained, with informed consent and institutional review board approval, from patients undergoing tumor resection or other surgical procedures at Vanderbilt University Medical Center, Cleveland Clinic Foundation, and the NIH. A total of 162 tissue samples from 127 patients, including 19 patients undergoing resective surgery for nonneoplastic disease and 29 grade II, 22 grade III, and 57 grade IV glioma patients, were analyzed. Patient information was collected, including gender, age, treatment received before and after surgery, extent of surgery, current status (alive, alive with progressive disease, deceased, and cause of death), and survival from the time of original pathologic diagnosis. Samples were collected at the time of surgery, immediately snap frozen in liquid nitrogen, and stored at –80°C until analysis. Histopathologic diagnoses were made by a neuropathologist, blinded to the original clinical diagnosis, from subsequent H&E-stained sections according to the 2000 WHO classification (1) as described previously (19).
Samples were prepared for MALDI analysis as described previously (19, 20). Briefly, frozen tissues were sectioned and transferred to MALDI target plates. Matrix droplets (0.1 µL saturated sinapinic acid in 50:50 acetonitrile/0.1% TFA in water, v/v) were blindly deposited on the surface of the sample, and the sections were dried. Optical section images were taken to align MS analysis regions with cellular morphology determined by histology. Samples were analyzed in a blinded fashion without knowledge of histologic diagnosis or clinical data.
Mass spectrometry analysis and data processing. Each matrix droplet was analyzed on a MALDI time-of-flight (TOF) Voyager DE-STR mass spectrometer (Applied Biosystems, Foster City, CA) as described previously (19). Spectra were internally mass calibrated using the singly and doubly charged ions for 
-hemoglobin (m/z 7,564.2 and 15,127.4, respectively), ubiquitin (m/z 8,565.8), and thymosin ß4 (m/z 4,964.5, previously identified in human glioblastoma xenographs; ref. 15). Mass spectra were baseline corrected, smoothed, and normalized. The peak lists from each individual biopsy or patient, depending on the analysis approach, were averaged to generate one general protein profile.
Statistical data analysis. Two independent supervised methods, symbolic discriminant analysis (SDA; ref. 21) and weighted flexible compound covariate method (WFCCM; ref. 22), were used to analyze the protein profiles. SDA applies genetic programming to determine discriminatory signals and builds functions using these signals that distinguish sample populations based on their classification. WFCCM applies multiple statistical tests to determine discriminatory markers. A linear combination of these markers is then generated that differentiates the sample groups. Further information is included in the supplementary data.
Protein marker identification. Two samples were used for protein identification, a glioma cell line and a human glioma tissue sample. The glioblastoma cell line, U118 MG (American Type Culture Collection, Manassas, VA), was cultured in DMEM supplemented with 10% FBS and harvested using an extraction buffer [0.25 mol/L sucrose, 0.01 mol/L Tris-HCl, 0.1 mmol/L PMSF (pH 7.6) at 4°C]. A cell aliquot was mixed 1:1 (v/v) with the extraction buffer, homogenized in an ice-chilled Duall homogenizer, and centrifuged at 10,000 x g for 10 minutes at 4°C. The supernatant was collected for protein identification. A glioblastoma (grade IV glioma) tissue was collected at the time of surgery, frozen, and stored at –80°C. The tissue was homogenized in T-PER extraction buffer (50 mg tissue/1 mL T-PER) in an ice-chilled Duall homogenizer and centrifuged at 16,000 x g for 30 minutes at 4°C. The supernatant was collected for protein identification.
Both samples were separated in two dimensions, first by ion exchange chromatography followed by reverse-phase HPLC. The cell line supernatant was separated by anion exchange chromatography using a HiTrap Q HP anion exchange column (Amersham Biosciences, Uppsala, Sweden) and a NaCl gradient (0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.45, 0.55, and 1 mol/L NaCl) based on the extraction solution. HPLC separation for selected fractions was achieved over a Vydac (Hesperia, CA) 214MS52 reverse-phase C4 column (5 µm particles, 2.1 mm x 25 cm) at 40°C using a linear gradient of 5% B to 20% B over 11 minutes, 20% B to 30% B over 15 minutes, 30% B to 55% B over 90 minutes, and 55% B to 95% B over 10 minutes. For fraction separation, solvent A was 0.1% TFA and solvent B was 0.1% TFA in acetonitrile. The tissue sample supernatant was separated by cation exchange chromatography using a HiTrap SP HP cation exchange column (Amersham Biosciences) with a linear gradient of 0% B to 100% B over 15 minutes, where A was 10 mmol/L ammonium acetate and B was 1 mol/L NaCl in 10 mmol/L ammonium acetate (pH 3.8 at room temperature). Selected fractions were separated over a Vydac 214MS5115 reverse-phase C4 column (5 µm particles, 1 mm x 15 cm) at 40°C using a linear gradient of 5% B to 25% B over 5 minutes, 25% B to 60% B over 50 minutes, and 60% B to 95% B over 20 minutes. Fractions were analyzed by MALDI MS for the markers of interest after each separation. HPLC fractions of interest were reconstituted in 0.1 mol/L ammonium bicarbonate and digested with trypsin (1:50, trypsin/protein, w/w; 37°C; 16-20 hours). Digested fragments were analyzed using either an Applied Biosystems 4700 MALDI TOF/TOF mass spectrometer or a ThermoLTQ ion trap mass spectrometer equipped with a Thermo Surveyor LC pump and a microelectrospray source (Thermo Electron, San Jose, CA).
MS and MS-MS spectra from MALDI TOF/TOF analysis were collected and the proteins were identified as described previously (23). The data were searched against the human National Center for Biotechnology Information database using the Mascot9 database search algorithm. A significance cutoff score of 65 was used. Analysis on the ThermoLTQ mass spectrometer was done using one full MS scan followed by three MS-MS scans of the three most intense ions. MS-MS spectra were searched against the human database using Sequest (Thermo Electron) and the Sequest search outputs were filtered using a custom-designed software tool called Complete Hierarchical Integration of Protein Searches using the following filtering criteria: cross-correlation (Xcorr) value of >1.0 for singly charged ions, >1.8 for doubly charged ions, and >2.5 for triply charged ions. In addition, a RSp (ranking of preliminary score) value of <5 and a Sp value (preliminary score) >350 were required for positive peptide identifications. A minimum of two peptide matches and a positive correlation between the m/z ratio detected and the molecular weight of the intact protein (including post-translational modifications) were also required for protein identification.
Immunohistochemistry. For immunofluorescence histochemistry, 18 µm thick sections were cut on a cryostat and incubated for 24 hours with PEA-15 antibodies (1:1,000). The sections were washed and incubated with Cy3-conjugated anti-mouse secondary antibodies (1:1,600; Jackson ImmunoResearch, West Grove, PA), washed, mounted, and coverslipped.
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Results |
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Pairwise comparisons were done on the training set to identify a subset of differentially expressed protein signals that best separated each classification: nontumor versus grades II, III, and IV biopsies; nontumor versus each individual tumor grade; grade II versus III; grade II versus IV; grade III versus IV; and grade II and III versus IV. Two independent methods were used for data analysis: SDA (21) and WFCCM (22). SDA uses genetic programming to build functions based on determined discriminatory signals, which distinguish sample classifications. WFCCM generates a model based on a linear combination of statistically determined discriminatory markers, which distinguishes sample groups.
For each analysis approach, a model was defined that best classified samples in the training data set. Based on the model, each patient was assigned a score using the expression, or signal intensity, of the determined biomarker signals; the accuracy of this classification scheme was verified on a blind data set (testing data set). The results from these analyses are summarized (Table 1). Classification and prediction accuracies are defined as the number of samples in the training and testing data sets, respectively, correctly classified. Biopsy protein patterns reflect a strong separation between tumor and nontumor tissues that extends to individual tumor grades. In all cases, nontumor tissues could be distinguished from gliomas with >92% classification accuracy. When comparing gliomas of different grades, the best separation was seen when comparing grade II and IV tumors (>93% classification accuracy), with slightly lower values for the grade III versus IV and grade II and III versus IV. The most difficult comparison was between grades II and III, which recapitulates the clinical situation. Accuracy limitations in protein profiling are due in part to the infiltrative nature of these tumors, the heterogeneous nature of the cells that comprise gliomas, and some methodologic limitations. Nonetheless, the results compare favorably with studies of interclass observer variability in pathology and neuropathology (24, 25).
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An independent agglomerative hierarchical clustering algorithm verified the statistically significant discriminator protein patterns, determined by WFCCM, in the training cohort for each of the classifications done. The results of three of these, nontumor versus tumor, nontumor versus grade IV tumor, and grade II versus grade IV tumor are shown (Fig. 2A-C, respectively). Clustering patterns reflect the strong correlation between the MS protein profile and the tissue classifications.
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Identifying glioma biomarkers. Discriminatory protein identification was done using two protein sources, the malignant human glioma cell line, U118 MG, and a primary human grade IV glioblastoma sample. Both the cells and the tissue sample were homogenized and proteins were separated using a two-dimensional liquid chromatography approach, consisting of an ion exchange separation followed by reverse-phase HPLC separation. Fractions were monitored by MALDI MS during separation for the m/z signals of interest. Selected fractions were digested and analyzed by either an Applied Biosystems 4700 MALDI TOF/TOF mass spectrometer or a ThermoLTQ ion trap mass spectrometer. Six proteins were identified, including calcyclin (m/z 10,092), dynein light chain 2 (m/z 10,262), calpactin I light chain (m/z 11,073), astrocytic phosphoprotein PEA-15 (m/z 15,035), fatty acid–binding protein 5 (m/z 15,076), and tubulin-specific chaperone A (m/z 17,268; Fig. 4A). Calcyclin, calpactin I light chain, and tubulin-specific chaperone A were identified as overexpressed in grade IV gliomas. On the other hand, astrocytic phosphoprotein PEA-15 was overexpressed in grade II and III tumors as opposed to grade IV gliomas and fatty acid–binding protein 5 was overexpressed in grade III tumors as opposed to grade IV. Calcyclin and dynein light chain 2 also discriminated between glioma survival subgroups with calcyclin predominant in STS patients and dynein light chain 2 overexpressed in LTS patients. The presence and relative expression levels for several of these proteins were verified by immunohistochemistry on intact tumor sections. For example, PEA-15 is shown to be in higher abundance in grade II astrocytomas compared with grade IV glioblastomas as recognized by the antibody staining pattern (Fig. 4B and C). This increase in protein expression correlates well with the presence of a MS signal at m/z 15,035 collected from a consecutive grade II tissue section as opposed to the loss of this signal in the grade IV section (Fig. 4D).
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Discussion |
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Because the accepted standard for glioma classification is histopathologic grading, we initially sought to validate the MS approach by identifying grade-specific biomarkers that correlated to histopathologically determined classifications. Tumors were subclassified by two neuropathologists, blinded to the original diagnosis, and analyzed by MALDI MS without knowledge of the original classification. Only samples with coincident clinical diagnoses were included in the analysis. Based on two independent classification approaches, WFCCM and SDA, MALDI MS provided specific proteomic patterns that classified glial tumors and nontumor brain tissue with high accuracy and precision. Proteomic profiles were used to discriminate between normal brain tissue and gliomas >92% of the time, with individual classification accuracies between normal tissues and individual grades ranging from 92% to 100%. In addition, MALDI MS patterns were used to distinguished glioma grades with high accuracy ranging from 76% to 97%. The most difficult distinction was between WHO grade II and III tumors, which mimics the clinical situation. Statistical analysis identified >100 potential, tumor-specific biomarkers. Validation of MS-based tumor classification using two different statistical techniques highlights the power of protein profiling for tissue characterization independent of the analysis approach.
WFCCM was also applied to identify MS-derived protein patterns that correlate to patient survival trends for all glioma patients and for a subgroup of patients with histologically confirmed glioblastoma multiforme. For all patients, standard treatment regimens were followed, including surgical tumor resection plus adjuvant radiotherapy and chemotherapy, as clinically indicated and tolerated. We show that a relatively small number of proteins can be used to distinguish between STS and LTS patients within the glioma patient population as a whole (P < 0.0001). Although these results are in line with previous clinical and pathologic studies, showing that the WHO grading system possesses discriminating predictive power, the protein pattern was an independent indicator of patient survival.
In addition, MALDI MS protein profiling was used to analyze a large group of patients with the most malignant form of glioma, glioblastoma multiforme, and found that the MS pattern from two m/z signals could further stratify patients into a STS group and a LTS group (P < 0.0001). For both analyses, the MALDI MS profile was the strongest determinant of survival in both univariate and multivariate analyses, stronger than most previously identified predictive variables, such as age, extent of resection, tumor grade, and use of adjuvant therapy. As expected, for the full glioma population, some overlap exists between grade-specific biomarkers and survival markers. Of the 24 discriminatory patient survival biomarkers for the entire glioma population, 17 were unique to the survival stratification. On the other hand, analysis of the glioblastoma multiforme population determined two unique markers that segregated the STS and LTS patients. These results suggest a novel approach to tissue classification based not on histopathologic features requiring visual analysis but on a molecular analysis of the protein patterns specific to the tissue sample.
Based on statistical analysis, several discriminatory proteins were identified, including calcyclin, dynein light chain 2, calpactin I light chain, astrocytic phosphoprotein PEA-15, fatty acid–binding protein 5 and tubulin-specific chaperone A. The MS signals from these proteins serve to discriminate gliomas from normal brain tissue and tumors of differing grade from one another; calcyclin and dynein light chain 2 also discriminated between glioma survival subgroups. These proteins are thought to be involved in several aspects of tumorigenesis. Calcyclin (S100A6), which plays a potential role in cell cycle progression and cell differentiation (26), is overexpressed in many tumors, especially at the margins of invasive cancers (27–30). Dynein light chain 2, a subunit of the microtubule-associated dynein motor complex, binds and sequesters Bim, a proapoptotic protein, to negatively regulate its apoptotic function (31). Calpactin I light chain (p11, S100A10) is expressed in many cancer cell lines (32, 33) and is thought to bind and stimulate plasminogen conversion to plasmin, a cell surface proteinase involved in tumor cell invasion and metastasis (34). PEA-15, an apoptosis inhibitor involved in several cell growth pathways (35, 36), is overexpressed in several tumor cell lines, including breast, larynx, cervix, and skin (36, 37), whereas studies have suggested overexpression of the fatty acid–binding protein 5 gene in prostate cancer tissue and cell lines (38, 39). Tubulin-specific chaperone A is a cofactor required for proper ß-tubulin folding (40).
Identification of these proteins was done in both a human glioblastoma cell line and a human glioblastoma tissue sample. These studies showed that, although cell lines are not ideal sources for protein identification, due to potential post-translational modifications and genomic mutations specific to the cell line, a positive correlation between the proteins identified from a cell line versus a tissue sample can exist. The identification of proteins from cell lines followed by further characterization of these proteins using traditional immunohistochemistry methods in intact tissues should serve as a valuable tool for protein identification and biomarker validation when resources are limited.
Our analysis has several potential limitations. A rank cutoff was used in WFCCM to determine the number of protein signals used in each classification. Therefore, the number of peaks reported is based not on the smallest or largest number of signals that could discriminate the classes but rather on an intermediate number based on statistical evidence. It may be possible to achieve a similar classification rate using a different subset of peaks. Although a variety of variables could lead to the misclassified samples, potential limitations include the diffuse cellular nature of the tumors as well as histopathologic inaccuracy. Furthermore, the tumors were not analyzed for genetic alterations suspected of playing a role in gliomagenesis, which may have prognostic significance, nor did we control for histologic homogeneity or require a specific treatment regimen. Although it may have been useful to focus this study on a homogeneous study population, the mixed nature of the tumors more faithfully corresponds to the clinical situation. Thus, this study represents an initial but important attempt to describe an unbiased molecular diagnostic tool that also possesses the power to predict overall outcome.
In summary, MALDI MS protein profiling has been used to determine protein expression patterns that distinguish primary gliomas from normal brain tissue and one grade of gliomas from another, with high sensitivity and specificity. In addition, we have shown that a small subset of protein signals can be used to predict survival of glioma patients as well as to identify differential survival patterns within a more homogenous population of glioblastoma multiforme patients. Additional studies are under way to enhance and expand the MALDI MS analysis of a larger spectrum of human gliomas as well as to identify and characterize additional known and suspected peptide/protein biomarkers. Once identified, characterization of the function and biological role of the specific proteins involved in the origin as well as the progression of gliomas may permit their use as diagnostic and prognostic markers. Because MALDI MS technology is capable of analyzing numerous samples, with analysis times of 5 minutes per sample, this technology is amenable to high-throughput tissue screening in a clinical setting.
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Acknowledgments |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
We thank Aurea Pimenta for growing the glioma cell lines, Salisha Hill and Lisa Zimmerman for assistance with the protein identification, Malin Andersson and Ariel Deutch for the immunohistochemistry, and Bashar Shakhtour, Jeremy Roberts, Bill White, and Huiming Li for their help in the bioinformatics analysis of these data.
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Footnotes |
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Received 8/20/04. Revised 6/30/05. Accepted 6/30/05.
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