Grade-dependent Proteomics Characterization of Kidney Cancer^*,S⃞

Materials—

After appropriate Institutional Review Board approval from University of California Davis, frozen and formalin-fixed paraffin-embedded (FFPE) clear cell renal cell carcinoma (ccRCC) tissues were obtained from the University of California Davis Medical Center in Sacramento, CA. The antibodies used in the study were mouse monoclonal anti-vimentin (VIM) (Dako), anti-SERPINH1 (Abcam), anti-phosphoglycerate kinase 1 (PGK1) (Santa Cruz Biotechnology), and anti-fructose-bisphosphate aldolase A (ALDOA) (Abnova) and rabbit polyclonal anti-ALDH1A1 (Abcam) and anti-AIFM1 (Abcam).

RCC Grading—

All the FFPE RCC samples were sectioned and counterstained with hematoxylin and eosin (H&E), and all were regraded by an experienced oncologic pathologist (A. D. B.) to standardize the grading of all specimens. The standard Fuhrman criteria of separation into four nuclear grades defined in order of increasing nuclear size, irregularity, and nucleolar prominence (3) were utilized.

Protein Extraction—

For comparison between frozen samples and FFPE tissues, protein was extracted utilizing the Liquid Tissue Protein Prep kit (Expression Pathology, Gaithersburg, MD). For protein preparation from frozen tissue, RCC grading was done based on the Fuhrman nuclear grade scale on H&E-stained slides and compared with the frozen block. Pieces of tissue were cut off the block and boiled at 95 °C for 90 min in 20 μl of Liquid Tissue buffer (Expression Pathology). Subsequently 1 μg of trypsin was added, and the samples were digested overnight at 37 °C. The samples were spun at 12,000 relative centrifugal force for 10 min, and the supernatant was transferred to a fresh tube. Protein preparation from FFPE tissues was performed with the Liquid Tissue Protein Prep kit (Expression Pathology) following the manufacturer's protocol.

For the grade-dependent proteomics experiments, the following protocol was used. Grading was done based on the FFPE sections counterstained with H&E. Unstained slides from adjacent sections were then deparaffinized and were used to collect regions of ∼0.5 cm in diameter with an assigned nuclear grade. 10-μm RCC FFPE sections were deparaffinized and hydrated through a xylene and ethanol series. The area corresponding to the adjacent H&E section was dissected using a 30-gauge needle and was transferred to 60 μl of RIPA buffer (150 mm NaCl, 10 mm Tris-HCl, pH 7.2, 2% SDS, 1% Triton X-100, 1% sodium deoxycholate, 5 mm EDTA). The sample was then heated at 100 °C for 20 min followed by a 3-h incubation at 80 °C with constant agitation to extract the protein (4). The samples were chilled on ice for 1 min, and chloroform/methanol precipitation was then performed to remove salt and detergents (5). After the addition of 40 μl of water, 400 μl of methanol was added, and after vortexing, 100 μl of chloroform was added. After vortexing, 300 μl of water was added. After a 1-min spin at 14,000 × g, the top aqueous layer was removed. Another 400 μl of methanol was added and mixed. After a 2-min spin at 14,000 × g, the supernatant was discarded, and the pellet was dried and suspended in 60 μl of 10 mm ammonium bicarbonate. Finally samples were trypsin-digested overnight with 1 μg of sequencing grade trypsin (Promega). The protein concentration of the digested frozen and FFPE RCC samples was quantified using the microBCA Protein Assay Reagent kit (Pierce), and a total of 3 μg from each sample was analyzed by LC-MS/MS.

Immunoblotting—

A section of tissue corresponding to what was used for LC-MS analysis was identified in an adjacent cut of the tissue block. These 10-μm FFPE sections were deparaffinized through a xylene and ethanol series, and 1 cm² of a tissue with known nuclear grade was harvested using a needle. The protein was decross-linked and extracted using the QProteome FFPE protein extraction kit (Qiagen) according to the manufacturer's protocol. Immunoblotting was performed as reported previously (6). Ponceau S staining was used to verify equal protein loading.

Immunohistochemistry—

FFPE slides were deparaffinized through a xylene and ethanol series and were treated with 3% hydrogen peroxide, methanol prior to the antigen retrieval in sodium citrate buffer, pH 6. Following blocking, the slides were incubated with the primary antibodies at 4 °C overnight. After PBS washes, the slides were incubated with biotin-conjugated secondary antibodies followed by the avidin-biotin complex (ABC Elite kit, Vector Laboratories) according to the manufacturer's instructions. The avidin-biotin complex was visualized using diaminobenzidine (Vector Laboratories). The sections were counterstained with hematoxylin and were coverslipped. Photographs were taken using 20× or 10× objective lenses on an Axiovert microscope (Carl Zeiss).

MS/MS Spectrometric Analysis—

Data were acquired using a Nano-LC-2D system (Eksigent) coupled with an LTQ (linear trap quadrupole) ion trap mass spectrometer (Thermo Finnigan) with separations accomplished on an in-laboratory fabricated fritless reversed phase microcapillary column (75 × 180 mm packed with Magic C18AQ, 3-mm beads with 100-Å pores; Michrom Bioresources) and vented column configuration. Each digested sample was transferred by the Eksigent autosampler to the on-line trap column (Zorbax 300SB C₁₈, 5 × 0.3 mm; Agilent) and desalted. Peptides were then eluted from the trap and separated by the aforementioned reversed phase microcapillary column at a flow rate of 300 nl/min and directly sprayed into the mass spectrometer. Buffer compositions used for reversed phase chromatography were as follows: buffer A, 0.1% formic acid in water; buffer B, 0.1% formic acid in 100% acetonitrile. Peptides were separated with a 48-min gradient (2–40% buffer B for 95 min, 40–80% buffer B for 12 min, and 80% buffer B for 13 min). MS/MS of the top 10 most intense ions was accumulated on the Thermo LTQ during each run. Peak lists were generated using the software Bioworks 3.3. The data were then analyzed with X!Tandem version 2008.01.01.1 and Ensembl human database version 49.36K (December 2006; 47,648 entries) based on human assembly NCBI 36 (October 2005). X!Tandem parameters were standard (fragment ion mass tolerance of 0.40 Da and a parent ion tolerance of 1.8 Da; iodoacetamide derivative of cysteine was specified as a fixed modification). Deamidation of asparagine and glutamine, oxidation of methionine and tryptophan, sulfone modification of methionine, tryptophan oxidation to formylkynurenine of tryptophan, and acetylation of the N terminus were specified as variable modifications. A threshold of −log(Expect scores) = 2.0 was used as a filtering criteria for X!Tandem; this is a standard value that is suitable for analysis with secondary statistical filtering such as clustering and enrichment calculation. Finally results were imported into the software Scaffold (version 2_00_02; Proteome Software Inc., Portland, OR) for protein identification validation, normalization, and comparison of spectral count or occurrences (that is, the number of all redundant peptide hits for a given protein). Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (7). Proteins were assigned by the Protein Prophet algorithm (8), and identifications were accepted if they could be established at greater than 95.0% probability and contained at least two identified peptides. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. The peptide false positive rate (FPR) was calculated using the Scaffold software. For each charge state, the incorrect assignments are tabulated to calculate the FPRi using the following formula: FPRi = ((Number assigned incorrect at 95% probability)/(Total number incorrect assigned)) × 100 where i is the charge state. The assignment is called correct if it is associated with a protein that has a 95% probability according to the Protein Prophet algorithm (13) and a minimum of two peptides each with a 95% probability based on the Peptide Prophet algorithm (10). The FPR is the sum of the values for each charge state.

Statistical and Pathway Analysis—

To assess accuracy of protein quantitation, results of proteomics analysis of 10 samples per grade were averaged, and a coefficient of variance was calculated for each identified protein. Significant grade-dependent differentials were filtered with a one-way analysis of variance (ANOVA) (9, 10) using thresholds of F-distribution cumulative probability of 0.05 or 0.01 (two standard filtering thresholds).

Statistically significant proteins were analyzed for molecular function, molecular process, and pathway enrichments using the Panther tools (11) and Gene Ontology. Network and metabolic pathways were analyzed using Ingenuity Pathways Analysis (IPA) (version 6; Ingenuity Systems Inc., Redwood City, CA). Statistically significant proteins were clustered using self-organizing maps (SOMs) with the software VisualGene (version 1.01.0024; Visipoint, Kuopio, Finland). SOMs are artificial neural networks based on an unsupervised algorithm (12). Protein levels of clusters were visualized in a heat map across grades using the script Pixelirator (University of California Davis Genome Center).

Frozen and FFPE Tissues Yield Similar Proteomics Results—

Initial experiments focused on the question of whether analysis of FFPE samples of RCC tissue yields proteomic profiles similar to those obtained from frozen samples. Because FFPE tissues are readily obtained and are considerably more abundant because of the availability of archived samples from pathology departments, such a finding would result in the ability to analyze substantially more samples. To address this issue, FFPE samples and their frozen tissue counterpart samples were both processed identically for the first part of this study.

Frozen tissues were obtained from nephrectomy specimens of three patients with confirmed (by oncologic pathologist A. D. B.) grade 2 ccRCC, and corresponding FFPE slides were obtained from the University of California Davis Pathology archives after appropriate Institutional Review Board approval. For the frozen samples, tumor grading was based on H&E slides from the corresponding frozen tumor; for FFPE tissues, H&E-stained tumor tissue obtained from the same block was scraped off of the slides as described under “Experimental Procedures.” All samples were processed with the Liquid Tissue® MS Protein Prep kit and subjected to tryptic digestion and tandem mass spectrometric analysis.

From these samples, 185 distinct proteins with two or more tryptic fragments were identified (supplemental Table 1). When proteomics analyses of FFPE and frozen samples were compared, the frozen samples yielded 168 proteins as compared with 143 for the FFPE processed samples, resulting in an overlap between the two data sets of 68% (Fig. 1). To further examine whether using FFPE samples would introduce bias, using the Panther libraries we analyzed the molecular function of the proteins identified from both tissue preparations. No significant differences in Panther molecular functions were observed between the two tissue sample preparations (Fig. 1). Oxidoreductases exhibited the most differences as expected because they are the most represented molecular function. Other functions are represented similarly in both tissue sample preparations.

Of the total 185 unique proteins identified in this experiment, only five proteins were significantly different (pairwise one-tailed t test p value <0.01) between FFPE and frozen tissue. All of these proteins, HSPA8, ANXA4, TUBB2C, YWHAB (14-3-3 β/α isoform), and TUBA1A, were found to be present at greater abundance in the FPPE samples. In addition, all of these proteins are classified by Gene Ontology as binding proteins. These experiments show that archived FFPE samples are adequate for proteomics analysis and biomarker discovery as compared with frozen tissue, and thus the readily available FFPE samples were utilized for subsequent analyses in this study.

Proteomics Analysis of ccRCC Yields Grade-specific Variations in Protein Quantities—

On the basis of the above results, additional proteomics analyses were performed on 50 FFPE tissues, which were evenly distributed among normal tissue and ccRCC of all Fuhrman grades (n = 10 per grade). Tumor tissues of confirmed grade were identified on FFPE slides in situ and removed by scraping, and proteomics analysis was performed by shotgun proteomics as described under “Experimental Procedures.” In the cases of four patients, both normal and cancer tissue was identified on the same slide; these tissues were analyzed separately.

A total of 1,470,313 spectra were analyzed, resulting in the identification of 777 proteins containing two or more peptide fragments. The rate of false positive for the filtering criteria used (minimum of two peptides at 95% confidence) is shown in supplemental Table 2 along with ages and genders of patients from whom the materials were obtained, the normalized spectral counts are shown in supplemental Table 3, the raw spectral counts are shown in supplemental Table 4, the unique peptide counts are shown in supplemental Table 5, the protein coverage is shown in supplemental Table 6, and the protein confidence is shown in supplemental Table 7. From the set of 777 identified proteins, 105 showed significant differences among the four Fuhrman grades and the normal kidney tissue using a one-way ANOVA (p value <0.01; Table I), and 180 proteins were significantly different using a slightly less stringent one-way ANOVA (p value <0.05; shown in supplemental Table 3).

Protein levels were determined by spectral count (13, 14) after normalization. Each protein spectral count (the number of all possibly redundant peptide hits for a given protein) was normalized across the 50 samples using the total number of spectra for each sample. In addition, four proteins that had the lowest inter- and intragrade variability, MYH9, tubulin β chain (TUBB) (shown in Fig. 2A), histone H4, and peptidylprolyl isomerase A, were chosen to be used as endogenous controls. Factors using the averages in a given tumor grade of these four proteins were used to normalize spectral count when comparing samples among grades. By way of example, spectral count variability is shown for three representative proteins that have distinct patterns of expression changes within grades (Fig. 2, B–D). In these examples, analysis by one-way ANOVA (p value <0.01) revealed statistically significant changes in proteins levels across grades.

To confirm the validity of the protein identification and quantitation methods used in this study, grade-specific changes of several proteins representative of the LC-MS data were determined by immunoblotting. To handle properly the variability between individual samples among grades (for examples, see Fig. 2), we compared four samples of three different grades, from normal tissue as well as grades 1 and 3, to confirm the LC-MS data. Four representative antibodies of proteins, whose average normalized spectral counts (n = 10) were significantly altered across grades, were utilized from the 10 samples. There was a high degree of consistency between the immunoblot protein band intensities and the normalized spectral count in all cases (Fig. 3). As further validation of the veracity of the spectral count method, each of the 18 proteins that were shown to be statistically differential in our previous work based on 2D gel electrophoresis and spot optical density (15) showed consistent quantitative results in the current study. Finally our cluster analysis showed striking cluster examples (see below) that could not have been achieved if the relative quantitation was inaccurate.

Immunohistochemistry using selected antibodies was also performed (Fig. 4), but the use of this technique was not applicable for validation due to protein compartmentalization (see Figs. 2 and 3). The MS and immunoblot protocols are based on a fairly crude sample collection where cells within the sample are mixed together, whereas the immunohistochemistry shows protein levels within specific cellular compartments.

As discussed above, the Fuhrman grading system for RCC utilizes the appearance of the nucleolus for stratification (3). Thus, the level of the nucleolar protein nucleophosmin was analyzed, and statistical significance was assessed utilizing a pairwise t test between normal tissue and grade 1 samples on one hand and samples of grades 2, 3, and 4 on the second hand (Fig. 5). Consistent with criteria for classification in the Fuhrman grading system and as further confirmation of the accuracy of the grading in this study, nucleophosmin appeared to be significantly increased in grade 2, 3, and 4 tumors as compared with normal tissue and grade 1 (p value = 0.0025).

Proteomics Analysis Shows Biochemical Processes and Pathways Enhanced in RCC—

Comparison of the proteins in all 50 samples that are statistically differential among all grades and normal tissue (supplemental Table 2) resulted in the identification of enriched functions (Table II), molecular processes (Table III), and metabolic pathways (Table IV) with a p value <0.01 after multiple testing correction. Pie charts with the molecular functions, the biological processes, and the metabolic and signaling pathways are shown in supplemental Figs. 1–3.

We note the abundance of dehydrogenases, such as ALDH4A1, ALDH1A1, ALDH6A1, ALDH9A1, and ALDH7A1, in the 105 RCC grade-dependent proteins corresponding to a significant enrichment of dehydrogenases in RCC biology (p value = 6.6e−22). These proteins, among others, are known to be involved in xenobiotic metabolism signaling, which itself exhibits an enrichment p value of 1.15e−4. Xenobiotic metabolism is associated with apoptosis and tumorigenesis and thus may be playing a critical role in RCC oncogenesis and response to therapy (see “Discussion”). Another set of identified proteins (TF, AMBP, fibronectin (FN1), FGB, FGG, and FGA) is part of the acute phase response signaling pathway, which is also enriched in this data set (p value = 1.33e−5). TF and AMBP have decreased levels in higher RCC grades, and FN1 has increased levels in higher RCC grades. FN1 has been associated with cell migration and could be involved in the metastatic process. Fibrin, the proteolytic product of fibrinogen, has been associated with positive regulation of cell proliferation, and an increased expression of this protein has been shown in various malignancies (see “Discussion”).

From the 180 proteins with ANOVA p value <0.05 (supplemental Table 3), 51 form a network using the IPA software (Fig. 6, lower left corner). Using the 27 from the list of 105 proteins with one-way ANOVA p value <0.01, we queried the IPA knowledge database to display 64 direct first-order neighbors in the IPA network. These 64 proteins have been associated to 85 of the 105 RCC grade one-way ANOVA p value <0.01 proteins. Among them, the network shows that p53, Myc, and HIF-1α are major hubs (Fig. 6, right). These proteins have known roles in kidney cancer oncogenesis and progression (see “Discussion”), and their presence in this ccRCC network diagram confirms the relevance of the 105-protein data set and their association to kidney cancer.

20 of the proteins significantly different among grades lie in the glycolysis pathway (Tables II and III). The highly significant processes and pathways altered in RCC that were found in this data set are consistent with what was found in previously published work using two-dimensional gel electrophoresis on frozen RCC samples from our (15) and other (16) laboratories. The changes in glycolysis pathway proteins observed in the present study converge on pyruvate (Fig. 7) demonstrating the importance of aerobic glycometabolism (the Warburg effect (16, 17)) in RCC. Furthermore we show (in red in Fig. 7) that cancer cells increase many of the proteins involved in glycolysis but also may use other related proteins or isoforms. For example EC 2.7.1.11 (6-phosphofructokinase) shows an increase of both PFKL and PFKP, whereas EC 3.1.3.11 (fructose bisphosphatase; FBP1), which governs the reverse reaction, is down-regulated. Similarly EC 4.2.1.11 (phosphopyruvate hydratase) shows an increase of both ENO1 and ENO2. For EC 4.1.2.13 (fructose-bisphosphate aldolase), there is an increase of ALDOA and ALDOC, whereas ALDOB is reduced, and for the anaerobic section of the glycolysis, EC 1.1.1.27 (l-lactate dehydrogenase), the LDHA level increases significantly, whereas LDHB decreases.

Self-organizing Mapping Shows Grade-specific Trends in Protein Levels—

It can be seen from the previous data that there exist a variety of patterns of protein levels as a function of tumor grade such that some proteins are most highly expressed in low grades, others are most highly expressed in high grades, and still others are most highly expressed in intermediate grades. Clustering using a 64-node self-organizing map algorithm as a function of the modulation of the protein level across grades (Fig. 8) revealed that the three chains of fibrinogen, which make up the fully assembled protein (18), form together a cluster node (Fig. 8, inset). Indeed FGA, FGB, and FGG are co-localized on chromosome 4 tightly in a 50-kb region, and they have been shown to be co-regulated by the transcription factor STAT3 (19). Similarly the α and β chain of ATP synthase clustered together, whereas the three other chains at a ratio of 1:3 of these two are present in neighboring cluster nodes with slightly different level patterns. These findings are consistent with the quaternary structure and regulation of these two proteins: this further demonstrates the validity of our methods and results.

From the cluster data using 180 proteins that were significantly different among grades (p value <0.05), we generated a Sammon graphic, representing the self-organizing map clustering with distance and placement of the nodes based on the similarity of the RCC grade protein level modulation (Fig. 9). This unsupervised neural network learning algorithm representation shows varying node sizes representative of the quantity of proteins that display the given pattern with the length of lines that link the nodes proportional to the degree of relatedness between groups (12). Protein levels from specific regions of similar patterns of expression corresponding to regions of the Sammon diagram are displayed in a heat map format (Fig. 9, insets). These heat maps have utility for diagnosis using immunohistochemistry of grade-specific protein abundance to confirm grade diagnosis in cases where pathological classification is ambiguous.