Are clear cell carcinomas of the ovary and endometrium phenotypically identical? A proteomic analysis

Human Pathology (2015) 46, 1427–1436

www.elsevier.com/locate/humpath

Original contribution

Are clear cell carcinomas of the ovary and endometrium phenotypically identical? A proteomic analysis☆,☆☆ Cynthia R. Fata MD, MSPH a , Erin H. Seeley PhD b , Mohamed M. Desouki MD, PhD a , Liping Du PhD c , Katja Gwin MD, PhD d , Krisztina Z. Hanley MD e , Jonathan L. Hecht MD, PhD f , Elke A. Jarboe MD g , Sharon X. Liang MD, PhD h , Vinita Parkash MD i , Charles M. Quick MD j , Wenxin Zheng MD k , Yu Shyr PhD c , Richard M. Caprioli PhD b , Oluwole Fadare MD l,⁎ a

Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232 Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232 c Vanderbilt-Ingram Cancer Center, Nashville, TN 37232 d Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390 e Department of Pathology, Emory University Hospital, Atlanta, GA 30322 f Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215 g University of Utah School of Medicine and ARUP Laboratories, Salt Lake City, UT 84112 h Department of Pathology and Laboratory Medicine, North Shore-LIJ Health System and Hofstra North Shore-LIJ School of Medicine, New Hyde Park, NY 11030 i Department of Pathology, Yale University School of Medicine, New Haven, CT 06610, and Bridgeport Hospital, Bridgeport, CT 06606 j Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR 72205 k Department of Pathology, University of Arizona College of Medicine, Tucson, AZ 85724 l Department of Pathology, University of California San Diego, San Diego, CA 92103 b

Received 30 March 2015; revised 3 June 2015; accepted 10 June 2015

Keywords: Clear cell carcinoma; MALDI IMS; Ovary; Endometrium; Mass spectrometry

Summary Phenotypic differences between otherwise similar tumors arising from different gynecologic locations may be highly significant in understanding the underlying driver molecular events at each site and may potentially offer insights into differential responses to treatment. In this study, the authors sought to identify and quantify phenotypic differences between ovarian clear cell carcinoma (OCCC) and endometrial clear cell carcinoma (ECCC) using a proteomic approach. Tissue microarrays were constructed from tumor samples of 108 patients (54 ECCCs and 54 OCCCs). Formalin-fixed samples on microarray slides were analyzed by matrix-assisted laser desorption/ionization mass spectrometry, and 730 spectral peaks were generated from the combined data set. A linear mixed-effect model with

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Conflict of interest and funding disclosures: none. This study was presented in a preliminary form at the 103rd Annual Meeting of the United States & Canadian Academy of Pathology, San Diego, CA, March 1-7, 2014. ⁎ Corresponding author at: UC San Diego Medical Center, Department of Pathology, 200 West Arbor Dr, MC 8720, Room 2-120, San Diego, CA 92103. E-mail address: [email protected] (O. Fadare). ☆☆

http://dx.doi.org/10.1016/j.humpath.2015.06.009 0046-8177/© 2015 Published by Elsevier Inc.

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C. R. Fata et al. random intercept was used to generate 93 (12.7%) peaks that were significantly different between OCCCs and ECCCs at the fold cutoffs of 1.5 and 0.667 and an adjusted P value cutoff of 1.0 × 10−10. Liquid chromatography–tandem mass spectrometry was performed on selected cores from each group, and peptides identified therefrom were compared with lists of statistically significant peaks from the aforementioned linear mixed-effects model to find matches within 0.2 Da. A total of 53 candidate proteins were thus identified as being differentially expressed in OCCCs and ECCCs, 45 (85%) of which were expressed at higher levels in ECCCs than OCCCs. These proteins were functionally diverse and did not highlight a clearly dominant cellular theme or molecular pathway. Although ECCCs and OCCCs are very similar, some phenotypic differences are demonstrable. Additional studies of these differentially expressed proteins may ultimately clarify the significance of these differences. © 2015 Published by Elsevier Inc.

1. Introduction Ovarian clear cell carcinoma (OCCC) is significantly more common than endometrial clear cell carcinoma (ECCC) and, therefore, has been studied more extensively. Ovarian and endometrial clear cell carcinomas are known to have broadly similar morphologic profiles [1,2], and previous studies have shown significant similarities between these 2 groups of tumors using gene expression profiling [3] and immunohistochemical [4] approaches. However, these broad similarities may belie subtle differences that may be important in understanding significant differences in pathogenesis and response to treatment. A significant subset of OCCCs is thought to originate from pelvic endometriosis [5,6]. Because the expression profiles of eutopic and ectopic endometria have been shown to be significantly different using a multitude of analytic modalities [7–9], it can be hypothesized that associated cancers similarly display significant differences, their morphologic similarities notwithstanding. In addition to selected molecular aberrations, differences have previously been reported between OCCCs and ECCCs regarding the expression of some fundamental proteins that are probably pathogenetically significant, including IMP3 [10,11], BAF250a/ARID1A [12–14], and p53 [15–18], among others. In endometrioid carcinomas, another endometriosis-associated malignancy in the ovary, some significant site-associated differences have been found. For example, McConechy et al [19] recently reported that PTEN mutations are more frequent in low-grade endometrial endometrioid carcinomas as compared with low-grade ovarian endometrioid carcinomas (67% versus 17%, respectively), whereas CTNNB1 mutations were significantly more common in ovarian endometrioid carcinomas (53% versus 28%). Matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) is a proteomic technology that can be used to spatially identify proteins or peptides in formalin-fixed, paraffin-embedded tissue. Sample preparation consists of treating the tissue or selected areas on the tissue with trypsin for in situ protein hydrolysis followed by the application of the MALDI matrix. A laser is fired at a tissue section mounted on a conductive slide; the resultant analytes are then separated in a time-of-flight analyzer. Analyte flight times are converted to m/z ratios, and a spectrum is generated.

Corresponding peptides can be identified by comparison to a known protein database. As a screening modality for potential biomarkers, MALDI IMS is advantageous over immunohistochemistry in that it does not require predetermined protein targets and can ultimately allow the identification of resultant proteins with high molecular specificity [20]. In this study, we used MALDI IMS and immunohistochemistry on cohorts of OCCC and ECCC to determine whether significant phenotypic differences exist between these groups and, if present, to quantify and characterize them.

2. Materials and methods 2.1. Case selection All components of this study were performed in compliance with relevant laws and institutional guidelines and were approved by the institutional review board at Vanderbilt University. In total, 54 cases of ECCC and 54 cases of OCCC were assembled from the archived files of the authors’ institutions after multiple layers of review by gynecologic pathologists. For each group, duplicate 1-mm core tissue microarrays (TMAs) were constructed using a manual array device (Beecher Instruments, Sun Prairie, WI) as previously described [21]. Details of case selection for the ECCC group and associated clinicopathologic data are outlined in previously published studies on that data set [22]. Unstained sections 4 μm thick were then obtained from the TMA and mounted on indium-tin oxide–coated glass slides for MALDI IMS and immunohistochemistry.

2.2. Matrix-assisted laser desorption ionization imaging mass spectrometry Samples were deparaffinized using xylenes and graded ethanol, and antigen retrieval using 10 mmol/L Tris (pH 9.0) was performed. Enzymatic digestion was performed by using a Portrait 630 acoustic robotic microspotter (Labcyte, Inc, Sunnyvale, CA). A solution of 0.075 mg/mL trypsin in 100 mmol/L ammonium bicarbonate (pH 7.6) with 9% acetonitrile was applied as discrete droplets of about 160 pL

Proteomic analysis of ovarian and endometrial clear cell carcinoma at 300-μm spacing across the surface of the TMA. A total of 40 drops were applied at each location with 120-second drying time between droplet applications. Subsequent to tryptic digestion, α-cyano-4-hydroxycinnamic acid matrix (5 mg/mL in 50% acetonitrile, 0.1% trifluoroacetic acid) was applied to the same locations using the Portrait spotter. A total of 50 droplets were applied to each location as 1 drop per pass. A digital image of the spotted TMA was taken, and mass spectral data were collected using a Bruker Autoflex Speed mass spectrometer (Bruker Daltonics, Billerica, MA) operated in reflectron positive ion mode. A total of 1600 laser shots were summed from each matrix spot with rastering occurring after each 50 shots to allow for complete sampling of each location. Mass spectral images were visualized using FlexImaging (Bruker) using an average spectrum for peak selection. A gynecologic pathologist (O. F.) determined the area of ECCC or OCCC for each core in the microarray that was compared with the image generated for the spectral peaks. Regions of interest from each core were extracted using FlexImaging, and the data were loaded into ClinProTools (Bruker) for preprocessing and peak selection. Spectra were baseline corrected, aligned, and normalized to total ion current before manual selections of boundaries for peak integration. An integrated peak table for all spectra collected from the TMA was exported for further statistical analysis.

2.3. Statistical analysis Patients have multiple spectra corresponding to multiple spots (the number varies from 1 to 46) of 1 tissue sample; thus, the spectra are not independent. For comparison of spectral data between OCCC and ECCC groups and to delineate the significantly different peaks, a linear mixed-effect model with random intercept was fit for each peak of the spectrum, with outcome as the peak area and group membership (ECCC or OCCC) as the only covariate. Effect size (mean peak area difference between the 2 groups and/or the ratio of mean peak area of ECCC to OCCC) and P values were obtained from each model. The P values were adjusted using the false discovery rate method. The fold cutoffs 1.5 and 0.667 (1/1.5) and the adjusted P value cutoff 1.0 × 10−10 were used. The peaks selected using these cutoffs were grouped according to the Euclidean distance of their peak areas (median for each patient) in a heat map. All the above statistical analyses were performed using R version 3.1.1 and packages “nlme” and “heatmap3.” For intergroup comparisons regarding histoscore, clinicopathologic factors, or immunoreactive frequencies, Student t or Fisher exact tests were applied.

2.4. Liquid chromatography–tandem mass spectrometry and protein identification Peptides were identified through the use of trypsin-swelled hydrogels as previously described [23]. Briefly, hydrogels were placed on representative cores and incubated for 4 hours

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at 50°C in a humidity chamber. Gels were removed and peptides extracted using alternating 50% acetonitrile and ammonium bicarbonate buffer. Extract was taken to dryness using a speed vac and resuspended in 1% formic acid for liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis. Peptides identified from the LC-MS/MS experiment were compared with lists of statistically significant peaks (from the aforementioned linear mixed-effects model) to find matches within 0.2 Da. Selected candidate identified proteins were verified by immunohistochemistry (see next section).

2.5. Immunohistochemistry For proteins that were deemed to be differentially expressed between OCCCs and ECCCs by MALDI IMS, additional validation by immunohistochemistry was performed for a pilot subset of 3 proteins. These 3 proteins were selected based on potential biological significance, fold difference between the 2 groups, and the accessibility of a commercially available antibody with reliable test performance. All immunohistochemical analyses were performed using the Leica (Buffalo Grove, IL) Bond Max autostainer. The primary antibodies that were ultimately used included annexin A4 (AnxA4, dilution 1:500, clone 1D3; Novus Biologicals, Littleton, CO), vimentin (prediluted, clone SLR33; Leica), and 14-3-3 protein beta/alpha (dilution 1:800, polyclonal; Novus Biologicals). Immunohistochemical scoring was performed by 2 authors (C. R. F. and O. F.) using the histoscore system, wherein a score is generated for each case that encompasses the extent of immunoreactive cells as well as the intensity of staining [24]. The histoscore is determined on a scale of 0 to 300 using the following formula: (3 × percentage of strongly staining cells) + (2 × percentage of moderately staining cells) + (1 × percentage of weakly staining cell). Scoring was limited to epithelial cells. Wherever the 2 cores for a given case show disparate scores, the highest score was selected for analytic purposes.

2.6. Classification imaging The annotated hematoxylin and eosin images were co-registered to the digital images used for mass spectral data collection. Regions corresponding to ECCCs and OCCCs were extracted from the image data. The data were loaded into ClinProTools (Bruker Daltonics Inc) in classes corresponding to their diagnoses. Spectral grouping per core was used. The data were processed and peaks picked as described above. A genetic algorithm classification model was generated using a leave 20% out cross-validation in lieu of an independent data set. A mutation rate of 0.2 and a crossover rate of 0.5 were used along with 3 K-nearest neighbors. The model was run for 10 iterations of 50 generations to determine the best classification model with a different random 20% left out each time. Once an optimized model was determined, the entire data set was classified and the results displayed in FlexImaging as a classification image where, instead of displaying individual

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ions, each pixel was color coded as to the class in which it was determined to belong.

3. Results 3.1. Clinicopathologic comparison The original data set included 54 patients from each group. These 2 groups are compared regarding basic clinicopathologic parameters in the Table. Patients with OCCCs were significantly younger than the ECCC patients (P = .03). As the predominant architectural pattern, the papillary pattern was significantly more common in OCCCs than in ECCCs (P = .0056), whereas the tubulocystic pattern was more common in ECCCs (P = .019). Endometriosis was present in 46% of the OCCC group. The 2 groups did not significantly differ regarding the other listed parameters.

3.2. Protein identification Spectral data were obtainable from 47 patient samples in the ECCC group. For the remaining 7 samples, data could not be obtained because of absence of significantly cellular material (abundant tumor necrosis, or minimal cellularity due to tumor stroma or a dominant cystic pattern). In the OCCC group, spectral data could be obtained from all 54 patient samples. We identified 730 spectral peaks from the combined data set. In the aforementioned linear mixed-effects models, 93 (12.7%) of the peaks showed statistically significant differences between

ECCC and OCCC groups at the fold cutoffs of 1.5 and 0.667 and the adjusted P value cutoff of 1.0 × 10−10 (Fig. 1). A heat map (Fig. 2) based on the peak area of these selected peaks graphically displays the related peaks and patients. The clusters of ECCC and OCCC patients are clearly separated, with only 3 ECCC cases among the OCCC cluster. These 93 peaks were then compared with the list of peptides that were identified from LC-MS/MS experiment to find matches within 0.2 Da. A total of 53 candidate proteins were identified. Of the 53, 8 (15%) were expressed at higher levels in OCCCs than ECCCs; and 45 (85%) were expressed at higher levels in ECCCs than OCCCs (Supplementary Table 1). In subsidiary analysis 1 comparing ECCCs (n = 47) with OCCCs associated with endometriosis (n = 25), 48 candidate proteins were ultimately identified as being differentially expressed, 83% of which were more highly expressed in ECCCs. In contrast, comparing ECCCs (n = 47) with OCCCs unassociated with endometriosis (n = 29) yielded 59 candidate proteins, all of which were more highly expressed in ECCCs (subsidiary analysis 2). There was substantial overlap in the proteins identified from these 2 analyses, and all 48 proteins in the first subsidiary analysis were present in the second analysis. Because 54 proteins were ultimately differentially expressed between OCCCs and ECCCs, 5 proteins were differentially expressed in the ECCC versus OCCC (unassociated with endometriosis) analysis that were not present in the ECCC versus OCCC (all cases) analysis, indicative of a loss of statistical significance for these proteins in the latter analysis. These 5 included brain acid soluble protein 1, clusterin, apolipoprotein A-I, transgelin-2, and transketolase.

3.3. Classification imaging Table Clinicopathologic comparison of the ECCC and OCCC groups Feature

ECCC

No. of patients 54 Patient age (y) Mean 65 Range 50-85 Endometriosis associated NA Necrosis present 26 (48%) Predominant architectural pattern Papillary 14 (26%) Solid 11 (20%) Tubulocystic 29 (54%) Mitotic index (mitotic figures/10 hpf) Median 1 Mean 2.4 Range 0 to 13 Stage distribution Early stage (FIGO stage I and II) 28 (52%) Late stage (FIGO stage III and IV) 26 (48%)

OCCC

P

54

NA

55.1 30-84 25 (46%) 31 (57.4%)

.03 NA NA NS

29 (54%) 9 (16.7%) 16 (29%)

.0056 NS .019

1 4.3 1 to 19

NS NS NA

34 (63%) 20 (37%)

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Abbreviations: ECCC, endometrial clear cell carcinoma; OCCC, ovarian clear cell carcinoma; hpf, high-power fields; FIGO, International Federation of Gynecology and Obstetrics; NA, not applicable; NS, not statistically significant.

A genetic algorithm classification model resulted in classification accuracies of 82.22% for OCCCs and 92% for ECCCs. This model was made up of 11 peptides (m/z 729.61, 1061.74, 1127.8, 1130.83, 1551.0, 1843.99, 1946.23, 1950.15, 1994.2, 2934.33, and 4041.97). The model was applied to classify both TMAs (ie, the OCCC and the ECCC), and the results are displayed as a classification image shown in Fig. 3. The classification images of both TMAs show a high degree of correlation with the histological diagnostic maps for each TMA also shown in the figure.

3.4. Immunohistochemistry validation Three of the 53 candidate proteins showing significant differences between ECCCs and OCCCs were assessed by immunohistochemistry, 2 of which were ultimately validated. Images from all 3 studies are outlined in Fig. 4, with the distribution of histoscores shown in Fig. 5. (a) Vimentin: Protein identification analyses had demonstrated that vimentin showed the highest fold difference between OCCCs and ECCCs, being 6.1 times more in the latter. By immunohistochemistry, vimentin was positive

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Fig. 1 Volcano plot comparing ECCCs with OCCCs. The red dots are the spectral peaks (93 total) at the fold cutoffs of 1.5 and 0.667 and the adjusted P value cutoff 1.0 × 10−10.

in 78% of ECCCs and 50% of OCCCs (P = .0047). The mean H-scores for ECCCs and OCCCs were 111 (±35.6) and 66 (±24.9), respectively (P = .039). (b) AnxA4: AnxA4 had been shown by protein identification analyses to be 2.0 higher in ECCCs than in OCCCs. By immunohistochemistry, these findings could not be validated. AnxA4 was positive in 88% of ECCCs and 84% of OCCCs. Both ECCCs (mean H-score, 180 ± 35.7) and OCCCs (mean H-score, 191 ± 29.5) exhibited strong and diffuse staining in most cases. There was no statistically significant difference in AnxA4 immunohistochemical staining (rates or H-scores) between ECCCs and OCCCs. (c) 14-3-3 beta/alpha: The 2.4-fold difference between ECCCs and OCCCs that was noted by protein identification analyses was validated by immunohistochemistry. Sixty-five percent of ECCCs (mean H-score, 69 ± 19.0) and 61% of OCCCs (mean H-score, 39 ± 13.9) were positive for 14-3-3 beta/alpha by immunohistochemical staining (P = .010 for H-score).

3.5. Lack of association with clinicopathologic parameters In subsidiary univariate analyses, the expression of vimentin, AnxA4, and 14-3-3 beta/alpha was each found not to be associated with patient age, tumor histologic patterns,

mitotic index, and stage in either group. In the OCCC group, the expression of these markers was also not associated with the presence or absence of endometriosis.

4. Discussion Understanding the differences between OCCCs and ECCCs may theoretically offer insights into clear cell carcinogenesis and pathogenesis at both sites, differential responses to directed therapies for afflicted patients, patient prognostication, and the extrapolability of research findings from one site to the other. Based on findings from previous studies using gene expression profiling and morphologic and immunohistochemical approaches, these tumors are known to be broadly similar [1–4]. However, if any differences are present, they may be highly significant. For example, recent studies indicate that mutations of telomerase reverse transcriptase promoter are similarly prevalent in ECCCs and OCCCs but that mutations at −124CNT are less frequent in ECCCs (33%) than in OCCCs (89%) [24]. Importantly, telomerase reverse transcriptase promoter mutation is an independently negative prognostic factor in early stage OCCCs but is not associated with clinicopathologic factors in ECCCs [24]. Similarly, several groups have shown that p53 alterations, which are uncommon in OCCCs, are present in up to 37.5% of ECCCs [14,17,18] and may define a more clinically aggressive, “serous-like” subset

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Fig. 2 Heat map comparing ECCC with OCCC patients. 1-47 (red), ECCC patients; 48-101 (green), OCCC patients. Heat map is based on the median peak area of each patient for each m/z (peak).

[14,25]. Mutations in the PIK3CA gene, which may upregulate the PIK3/AKT/mTOR pathway and which accordingly is a potential target for directed therapies, has been identified in 39% to 43% of OCCCs [26,27] but in only 18.8% [17] of ECCCs. These studies illustrates the potential clinical significance of establishing whether treatment modalities developed for OCCCs can be extrapolated to patients with the more uncommon ECCCs. In this study, we used a proteomic approach to investigate phenotypic differences between ECCCs and OCCCs. We identified 93 spectral peaks—representative of 12.7% of all peaks from the combined data set of our cases—with statistically significant differences between ECCCs and OCCCs; the majority (85%) of the 53 subsequently identified proteins was expressed at higher levels in ECCCs than in OCCCs. The 53 proteins were diverse and did not highlight a specific cellular theme or molecular pathway that is differentially operational in a dominant fashion between the 2 groups. In addition, the proteins did not show significant and obvious overconnectivity, as assessed on the Harvard interactome database (http://interactome.dfci.harvard.edu/). The 15 proteins showing highest fold difference between OCCCs and ECCCs included cytoskeletal (vimentin) and cytoskeletal linker (plectin) proteins; 4 isoforms of the multifunctional 14-3-3

family of proteins (which mediate signal transduction by binding to phosphoserine-containing proteins); a member of a protein-folding complex (T-complex protein 1 subunit gamma); an enzyme of the nonoxidative pentose phosphate pathway, one of whose functions is to maintain glutathione at a reduced state and thus protect sulfhydryl groups and cellular integrity from oxygen radicals (transaldolase); peroxiredoxin-1 and peroxiredoxin-4, which are 2 members of the peroxiredoxin family of antioxidant enzymes (which reduce hydrogen peroxide and alkyl hydroperoxide); AnxA4 (see below); antithrombin III, a regulator of the coagulation cascade; Prelamin-A/C, a precursor to the lamin proteins, which may be involved in nuclear stability, chromatin structure, and gene expression; plasma protease C1 inhibitor, a regulator of the complement cascade; and plasminogen activator inhibitor 1 RNA-binding protein, which is the regulator of the stability of plasminogen activator inhibitor 1, a protein that plays a role in tumor invasiveness and metastasis, as well as angiogenesis, fibrinolysis, and wound healing. Vimentin, AnxA4, and 14-3-3 protein beta/alpha were ultimately selected for validation studies by immunohistochemistry. Vimentin showed the highest fold difference between the 2 groups, being more than 6 times higher in ECCCs as compared with OCCCs. Our immunohistochemical studies

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TMA 1

TMA 2

OCCC ECCC Mass Spec Classification Image

Histological Diagnosis

Fig. 3 Imaging mass spectrometry classification images. Each pixel of each image is color coded as to its classification via the genetic algorithm classification model. Pink corresponds to OCCC, whereas green corresponds to ECCC. On the left are the mass spectrometry classification images, whereas on the right are the corresponding histological diagnosis maps. Not all arrays are shown. Only tumorous areas were classified.

showed that 50% of OCCCs and 78% of ECCCs expressed vimentin (which is consistent with previously published literature [28,29]) and that the semiquantitatively determined general protein level of vimentin was significantly higher

in ECCCs. How vimentin is differentially involved in the tumorigenesis or pathogenesis of ECCCs is unclear. One possibility is that epithelial-to-mesenchymal transition, which typically denotes upregulation of vimentin [30], is more

Fig. 4 Immunohistochemical validation of vimentin, AnxA4, and 14-3-3 beta/alpha in 4 cases of ECCC (top row) and 4 cases of OCCC (bottom row). A, Hematoxylin and eosin. B, Vimentin. C, AnxA4. D, 14-3-3 beta/alpha (original magnifications ×10).

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Fig. 5 Distribution of histoscores for vimentin, AnxA4, and 14-3-3 beta/alpha for ECCCs and OCCCs.

activated in ECCCs. We have previously shown that markers of epithelial-to-mesenchymal transition are expressed robustly in ECCCs [31]. Vimentin may also be directly involved in the pathogenesis of a subset of cases through other mechanisms. A recent study of bladder cancer cell lines found that vimentin, through its interaction with cytoskeletal linker protein plectin, directly facilitates cancer invasion and metastases [32]. Parenthetically, in the current study, plectin was also one of the more differentially expressed proteins, being 1.942 times higher in ECCCs as compared with OCCCs. Four different isoforms of the 14-3-3 protein showed differential expression between ECCCs and OCCCs by mass spectrometry; 14-3-3 beta/alpha, which was found to be 2.408 times higher in ECCCs than in OCCCs by mass spectrometry, was ultimately validated by immunohistochemistry. 14-3-3 beta/alpha is a multifunctional and adapter protein that is involved in the regulation of a large spectrum of signaling pathways [33–38]. 14-3-3 beta/alpha has previously been shown to be a putative biomarker of response to neoadjuvant

C. R. Fata et al. chemotherapy in estrogen receptor–positive breast cancer [33,34]; a discriminant between thyroid follicular carcinoma and adenoma [35]; a urinary biomarker of renal cell carcinoma [36]; a potential prognostic marker in lung adenocarcinoma [37]; and a differentially expressed, potentially discriminatory serum biomarker between benign and malignant ovarian serous tumors [38]. Additional studies are needed to determine if 14-3-3 protein beta/alpha is also a marker of chemoresistance in ECCCs and OCCCs. AnxA4 was found to be 2.053 higher in ECCCs as compared with OCCCs by our protein identification studies, but these findings could not be validated by immunohistochemistry. AnxA4 was expressed at very high levels in both ECCCs (mean H-score, 180) and OCCCs (mean H-score, 191); and where present, staining was often strong and diffuse (Fig. 4). Therefore, it is possible that our immunohistochemical method of semiquantifying AnxA4 expression is not sufficiently sensitive in detecting differences between the groups when high levels of AnxA4 expression are present in both groups. AnxA4 is a calcium/phospholipid-binding protein that promotes membrane fusion and is involved in exocytosis and regulation of epithelial Cl− secretion. Previous studies using a variety of analytic approaches have shown that AnxA4 is expressed at significantly higher levels in OCCCs as compared with non– clear cell ovarian cancers [39,40]. AnxA4 is highly involved in conferring cisplatin chemoresistance to OCCCs by promoting the efflux of platinum drugs from tumor cells, possibly through the copper transporter ATP7A, and is accordingly a promising therapeutic target for chemoresistant cases [41–43]. Our current finding shows that AnxA4 is also highly expressed in ECCCs (88%; mean H-score, 180) at comparable levels to OCCCs (84%; mean H-score, 191), suggesting that the same issues of chemoresistance associated with AnxA4 in OCCCs may also apply to ECCCs. Given that, by most measures, OCCCs and ECCCs appear to be largely similar, we critically considered the possibility that the differences noted between the groups in our study are indicative of a preanalytic artifact reflecting a differential burden of non-CCC histotypes in the ECCC group rather than a true biologic phenomenon. It is well known that the interobserver variability in the pathologic classification of high-grade endometrial carcinomas is significantly higher than their ovarian counterparts [1]. First, we have sought to minimize diagnostic variability by requiring consensus on histotypic classification of all cases by at least 2 independent gynecologic pathologists [22]. Second, if there is a disproportionate burden of non-CCC histotypes in the ECCC arm, it may be anticipated that there would be comparatively decreased expression of markers associated with the clear cell histotype, such as the aforementioned AnxA4, in the ECCC arm as compared with the OCCC group. However, we found no significant differences between the 2 groups regarding expression of AnxA4; and indeed, the raw AnxA4 histoscore for the OCCC group was slightly higher. Nevertheless, in the absence of an entirely objective external validator, the role of diagnostic variability cannot be entirely discounted. Our cases,

Proteomic analysis of ovarian and endometrial clear cell carcinoma at minimum, are indicative of the cases that contemporary pathologists classify as CCC and on which patient treatment decisions are made. Differences between ECCCs and OCCCs may be attributable, at least partially, to the microenvironment in which a significant subset of OCCCs, but not ECCCs, arises. Of the OCCC cases included in this study, 46% were associated with endometriosis. The internal confines of endometriotic cysts have been shown to have a high concentration of free iron, lactose dehydrogenase, potential antioxidant, lipid peroxide, reactive oxygen species, and 8-hydroxy-2′-deoxyguanosine [44]. Repeated intracystic hemorrhage may facilitate ironmediated oxidative stress, which may then modify genomic DNA and eventuate in carcinogenesis [44]. In addition, because tumor microenvironment has been shown to induce specific gene expression profiles in OCCCs [45], slight phenotypic differences between OCCCs and ECCCs may be expected. However, in our study, we could identify no significant phenotypic differences between the OCCCs with or without associated endometriosis for the few markers that were immunohistochemically validated. Paradoxically, some of the proteins that were more highly expressed in ECCCs are either antioxidant enzymes (peroxiredoxins) or are involved in the protection of cells from oxygen radicals (transaldolase). This raises the intriguing possibility that some of these proteins are either differentially upregulated in ECCCs or are downregulated in OCCCs in a manner that contributes to tumorigenesis. However, we ultimately could not clearly demonstrate a role for endometriosis in explaining the differences noted in this study. In summary, our findings affirm that clear cell carcinomas of the ovary and endometrium have broad phenotypic similarities; in this study, 87.3% of mass spectral data derived from OCCCs and ECCCs could not be statistically distinguished. However, some phenotypic differences exist that suggest that the 2 groups analyzed in this study are not entirely identical. We identified a total of 53 functionally diverse proteins that were differentially expressed between OCCCs and ECCCs. These differences may be reflective of the different environments in which the tumors develop. Future studies using proteomic and other investigative approaches may help to identify which of these phenotypic differences are significant and reflect the primary molecular drivers in the development and progression of these enigmatic tumors.

Supplementary data Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.humpath.2015.06.009.

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