Protein kinase activity is associated with CD63 in melanoma cells

BACKGROUND: The tetraspan protein CD63, originally described as a stage-specific melanoma antigen but also present in a number of normal cells, regulates melanoma cell growth in nude mice, motility in serum containing media, and adhesion to several extracellular matrix proteins. CD63 has been reported to associate with β1 and β2 integrins, but the mechanism of signal transduction by CD63 is not clear. This study examined whether CD63 is associated with protein kinase and can transmit signals in melanoma cells. METHODS: Immunoprecipitation and radiolabeling were used to test for association of protein kinase activity with CD63. Adhesion of cells to monoclonal antibodies immobilized to microtiter plates was used to examine the ability of CD63 to transmit signals. RESULTS: CD63 was capable of transmitting a signal in melanoma cells that required extracellular calcium. In the absence of extracellular calcium at the time of binding to the CD63 mAb, the cell was no longer responsive to stimulation by CD63. Immunoprecipitation studies demonstrated protein kinase activity associated with CD63, and phosphoamino acid analysis revealed that most of this protein kinase activity was due to serine kinase activity. CONCLUSION: The current study suggests that serine protein kinase activity associated with CD63 may play a role in signaling by CD63 in melanoma cells

Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors

The heterogeneity that soft tissue sarcomas (STS) exhibit in their clinical behavior, even within histological subtypes, complicates patient care. Histological appearance is determined by gene expression. Morphologic features are generally good predictors of biologic behavior, however, metastatic propensity, tumor growth, and response to chemotherapy may be determined by gene expression patterns that do not correlate well with morphology. One approach to identify heterogeneity is to search for genetic markers that correlate with differences in tumor behavior. Alternatively, subsets may be identified based on gene expression patterns alone, independent of knowledge of clinical outcome. We have reported gene expression patterns that distinguish two subgroups of clear cell renal carcinoma (ccRCC), and other gene expression patterns that distinguish heterogeneity of serous ovarian carcinoma (OVCA) and aggressive fibromatosis (AF). In this study, gene expression in 53 samples of STS and AF [including 16 malignant fibrous histiocytoma (MFH), 9 leiomyosarcoma, 12 liposarcoma, 4 synovial sarcoma, and 12 samples of AF] was determined at Gene Logic Inc. (Gaithersburg, MD) using Affymetrix GeneChip® U_133 arrays containing approximately 40,000 genes/ESTs. Gene expression analysis was performed with the Gene Logic Genesis Enterprise System® Software and Expressionist software. Hierarchical clustering of the STS using our three previously reported gene sets, each generated subgroups within the STS that for some subtypes correlated with histology, and also suggested the existence of subsets of MFH. All three gene sets also recognized the same two subsets of the fibromatosis samples that we had found in our earlier study of AF. These results suggest that these subgroups may have biological significance, and that these gene sets may be useful for sub-classification of STS. In addition, several genes that are targets of some anti-tumor drugs were found to be differentially expressed in particular subsets of STS

Disaggregation and invasion of ovarian carcinoma ascites spheroids

BACKGROUND: Malignant ascites often develops in advanced stages of ovarian carcinoma, consisting of single and aggregated tumor cells, or spheroids. Spheroids have commonly been used as tumor models to study drug efficacy, and have shown resistance to some chemotherapies and radiation. However, little is known about the adhesive or invasive capabilities of spheroids, and whether this particular cellular component of the ascites can contribute to dissemination of ovarian cancer. Here, we examined the invasive ability of ascites spheroids recovered from seven ovarian carcinoma patients and one primary peritoneal carcinoma (PPC) patient. METHODS: Ascites spheroids were isolated from patients, purified, and immunohistochemical analyses were performed by a pathologist to confirm diagnosis. In vitro assays were designed to quantify spheroid disaggregation on a variety of extracellular matrices and dissemination on and invasion into normal human mesothelial cell monolayers. Cell proliferation and viability were determined in each assay, and statistical significance demonstrated by the student's t-test. RESULTS: Spheroids from all of the patients' ascites samples disaggregated on extracellular matrix components, with the PPC spheroids capable of complete disaggregation on type I collagen. Additionally, all of the ascites spheroid samples adhered to and disaggregated on live human mesothelial cell monolayers, typically without invading them. However, the PPC ascites spheroids and one ovarian carcinoma ascites spheroid sample occasionally formed invasive foci in the mesothelial cell monolayers, suggestive of a more invasive phenotype. CONCLUSION: We present here in vitro assays using ascites spheroids that imitate the spread of ovarian cancer in vivo. Our results suggest that systematic studies of the ascites cellular content are necessary to understand the biology of ovarian carcinoma

Quantitative proteomic analysis by iTRAQ® for the identification of candidate biomarkers in ovarian cancer serum

<p>Abstract</p> <p>Background</p> <p>Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ^®.</p> <p>Results</p> <p>Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ^®to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified.</p> <p>Conclusions</p> <p>This study provides the first analysis of immunodepleted serum in combination with iTRAQ^®to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.</p

Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring

<p>Abstract</p> <p>Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/1649068103671302</url></p

Leucine-rich alpha-2-glycoprotein-1 is upregulated in sera and tumors of ovarian cancer patients

<p>Abstract</p> <p>Background</p> <p>New biomarkers that replace or are used in conjunction with the current ovarian cancer diagnostic antigen, CA125, are needed for detection of ovarian cancer in the presurgical setting, as well as for detection of disease recurrence. We previously demonstrated the upregulation of leucine-rich alpha-2-glycoprotein-1 (LRG1) in the sera of ovarian cancer patients compared to healthy women using quantitative mass spectrometry.</p> <p>Methods</p> <p>LRG1 was quantified by ELISA in serum from two relatively large cohorts of women with ovarian cancer and benign gynecological disease. The expression of LRG1 in ovarian cancer tissues and cell lines was examined by gene microarray, reverse-transcriptase polymerase chain reaction (RT-PCR), Western blot, immunocytochemistry and mass spectrometry.</p> <p>Results</p> <p>Mean serum LRG1 was higher in 58 ovarian cancer patients than in 56 healthy women (89.33 ± 77.90 vs. 42.99 ± 9.88 ug/ml; p = 0.0008) and was highest among stage III/IV patients. In a separate set of 193 pre-surgical samples, LRG1 was higher in patients with serous or clear cell ovarian cancer (145.82 ± 65.99 ug/ml) compared to patients with benign gynecological diseases (82.53 ± 76.67 ug/ml, p < 0.0001). CA125 and LRG1 levels were moderately correlated (r = 0.47, p < 0.0001). <it>LRG1 </it>mRNA levels were higher in ovarian cancer tissues and cell lines compared to their normal counterparts when analyzed by gene microarray and RT-PCR. LRG1 protein was detected in ovarian cancer tissue samples and cell lines by immunocytochemistry and Western blotting. Multiple iosforms of LRG1 were observed by Western blot and were shown to represent different glycosylation states by digestion with glycosidase. LRG1 protein was also detected in the conditioned media of ovarian cancer cell culture by ELISA, Western blotting, and mass spectrometry.</p> <p>Conclusions</p> <p>Serum LRG1 was significantly elevated in women with ovarian cancer compared to healthy women and women with benign gynecological disease, and was only moderately correlated with CA125. Ovarian cancer cells secrete LRG1 and may contribute directly to the elevated levels of LRG1 observed in the serum of ovarian cancer patients. Future studies will determine whether LRG1 may serve as a biomarker for presurgical diagnosis, disease recurrence, and/or as a target for therapy.</p

Interdependency of CEACAM-1, -3, -6, and -8 induced human neutrophil adhesion to endothelial cells

Members of the carcinoembryonic antigen family (CEACAMs) are widely expressed, and, depending on the tissue, capable of regulating diverse functions including tumor promotion, tumor suppression, angiogenesis, and neutrophil activation. Four members of this family, CEACAM1, CEACAM8, CEACAM6, and CEACAM3 (recognized by CD66a, CD66b, CD66c, and CD66d mAbs, respectively), are expressed on human neutrophils. CD66a, CD66b, CD66c, and CD66d antibodies each increase neutrophil adhesion to human umbilical vein endothelial cell monolayers. This increase in neutrophil adhesion caused by CD66 antibodies is blocked by CD18 mAbs and is associated with upregulation of CD11/CD18 on the neutrophil surface. To examine potential interactions of CEACAMs in neutrophil signaling, the effects on neutrophil adhesion to human umbilical vein endothelial cells of a set of CD66 mAbs was tested following desensitization to stimulation by various combinations of these mAbs. Addition of a CD66 mAb in the absence of calcium results in desensitization of neutrophils to stimulation by that CD66 mAb. The current data show that desensitization of neutrophils to any two CEACAMs results in selective desensitization to those two CEACAMs, while the cells remain responsive to the other two neutrophil CEACAMs. In addition, cells desensitized to CEACAM-3, -6, and -8 were still responsive to stimulation of CEACAM1 by CD66a mAbs. In contrast, desensitization of cells to CEACAM1 and any two of the other CEACAMs left the cells unresponsive to all CD66 mAbs. Cells desensitized to any combination of CEACAMs remained responsive to the unrelated control protein CD63. Thus, while there is significant independence of the four neutrophil CEACAMs in signaling, CEACAM1 appears to play a unique role among the neutrophil CEACAMs. A model in which CEACAMs dimerize to form signaling complexes could accommodate the observations. Similar interactions may occur in other cells expressing CEACAMs

Clustering of gene expression of the MFH samples with the RCC gene set (A), OVCA gene set (B), AF gene set (C), and the protein kinase gene set (D) as described in the text

The 16 MFH samples were clustered using the Eisen clustering software Cluster as described in the text. MFH-1 to MFH-9 grouped together in panel A and are indicated by an asterisk. The tissue samples in the tree are joined by very short branches if they have gene expression patterns that are very similar to each other, and by increasingly longer branches as their similarity decreases.<p><b>Copyright information:</b></p><p>Taken from "Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors"</p><p>http://www.translational-medicine.com/content/6/1/23</p><p>Journal of Translational Medicine 2008;6():23-23.</p><p>Published online 6 May 2008</p><p>PMCID:PMC2412854.</p><p></p

Clustering of gene expression of the STS samples with the RCC gene set (A), OVCA gene set (B), and AF gene set (C)

The 25 STS samples were clustered using the Eisen clustering software Cluster as described in the text and are labeled as in Figure 2. The tissue samples in the tree are joined by very short branches if they have gene expression patterns that are very similar to each other, and by increasingly longer branches as their similarity decreases.<p><b>Copyright information:</b></p><p>Taken from "Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors"</p><p>http://www.translational-medicine.com/content/6/1/23</p><p>Journal of Translational Medicine 2008;6():23-23.</p><p>Published online 6 May 2008</p><p>PMCID:PMC2412854.</p><p></p