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Laboratory of Biological Chemistry, Gerontology Research Center, National Institute on Aging, Baltimore, Maryland 21224 [C. D. H., C. A. S-B., P. J. M.]; Departments of Pathology [E. S. P., P. J. M.] and Gynecology [F. J. M.], The Johns Hopkins Medical Institutions, Baltimore, Maryland 21287; Gynecologic Oncology Center, Mercy Medical Center, Baltimore, Maryland 21202 [D. D. I., N. B. R.]; Department of Pathology, The University of Michigan Medical School, Ann Arbor, Michigan 48109 [K. R. C.]; and Department of Pathology, Duke University Medical Center, Durham, North Carolina 27710 [G. J. R.]
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ABSTRACT |
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 Top ABSTRACT IntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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Introduction |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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Candidate gene approaches as well as various subtractive and comparative methods have led to the identification of several genes differentially expressed in ovarian cancer. Overexpressed genes include c-erbB2 (4) , Bcl-2 (5) , and cyclin D1 (6) , whereas DOC-1, DOC-2 (7) , LOT1 (8) , and OVCA1 (9) have been found to be underrepresented in ovarian cancer. In recent years, powerful techniques have been developed that allow comprehensive analysis of gene expression. Many thousands of genes can be monitored simultaneously for changes in their expression levels using cDNA array technologies. Such techniques have recently been applied to ovarian cancer. For example, arrays containing 5,766 cDNAs (10) and 21,500 cDNAs (11) have been used to identify hundreds of genes differentially expressed in ovarian cancer, some of which, such as the secreted protease inhibitor HE4, represent promising tumor markers.
SAGE3 is another powerful technique that allows large-scale analysis of gene expression in a tissue of interest. In contrast to array methodologies, SAGE does not require a priori knowledge of the expressed genes in the starting material and leads to an unbiased comprehensive representation of the transcripts present in a sample (12 , 13) . Comparisons of the global gene expression patterns generated by SAGE allow the identification of differentially expressed genes.
As described above, experimental evidence for the origin of ovarian cancer is sparse. The most widely accepted tissue of origin, the OSE, consists of a simple epithelial layer covering the ovaries and is, unfortunately, not easily amenable to experimental manipulations. For these reasons, we have chosen to study gene expression in a wide collection of ovarian tissues. Several ovarian carcinoma-derived cell lines and primary serous ovarian carcinomas were used as malignant specimens. Nonmalignant control specimens consisted of a short-term culture of OSE cells as well as immortalized OSE and cystadenoma cells. In the study reported here, we have, for the first time, used SAGE to generate global expression profiles of the ovarian samples. We have compared the gene expression profiles of ovarian cancer and normal ovarian tissue and identified numerous differentially expressed genes. In addition to genes implicated in ovarian cancer for the first time, several genes previously known to be up- or down-regulated in ovarian cancer are identified in our comparisons. Further validation of selected SAGE data are accomplished through immunohistochemical analysis of candidate gene products.
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Materials and Methods |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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Three high-grade serous ovarian cancer specimens OVT6, OVT7, and OVT8, composed of at least 80% tumor cells as determined by histopathology, were chosen for SAGE. The ovarian tumor samples were frozen immediately after surgical resection and were obtained form the Johns Hopkins gynecological tumor bank in accordance with institutional guidelines on the use of human tissue. Normal human ovarian surface epithelial (HOSE-4) cells were cultured from the right ovary of a patient undergoing hysterectomy and bilateral salpingo-oophorectomy for benign disease. The OSE cells were obtained by gently scraping the surface of the ovary with a cytobrush and were grown for two passages in RPMI 1640 supplemented with 10% FBS and 10 µg/ml epidermal growth factor.
SAGE.
Total RNA was obtained from guanidinium isothiocyanate cell
lysates by centrifugation on CsCl. Polyadenylated mRNA was purified
from total RNA using the Messagemaker kit (Life Technologies),
and the cDNA was generated using the cDNA Synthesis System (Life
Technologies). For the "POOL" library, 100 µg of total RNA
from each of 10 ovarian cancer cell lines (A222, A2780, AD10, BG-1,
ES-2, MDAH 2774, OVCA432, OV1063, UCI101, and UCI107) were combined,
and the mRNA was purified. SAGE was performed essentially as described
(12)
for all of the libraries except HOSE. To create the
HOSE library, MicroSAGE, a modified SAGE technique developed for
limited sample sizes (16)
, was used. Approximately
1 x 106 OSE cells in short-term
culture were lysed, and the mRNA was purified directly using Oligo
(dT)25 Dynabeads (Dynal, Oslo, Norway). As
part of the CGAP SAGE consortium, the SAGE libraries were arrayed at
the Lawrence Livermore National Laboratories and sequenced at the
Washington University Human Genome Center or the National Institute
Sequencing Center (NIH, Bethesda, MD). The data have been posted
on the CGAP
website4
as part of the SAGEmap database (17)
.
Sequence data from each library were analyzed by the SAGE software
(12)
to quantify tags and identify their corresponding
transcripts. The data for the colon libraries NC1, NC2, Tu98, Tu102,
HCT116, and SW837 were obtained from the SAGEmap database and analyzed
in the same way. Because the different libraries contained various
numbers of total tags, normalization (to 100,000 tags) was performed to
allow meaningful comparisons. The 10,000 most highly expressed genes in
the 16 SAGE libraries of interest were formatted in a Microsoft Excel
spreadsheet, and Pearson correlation coefficients were calculated for
each pair-wise comparison using normalized tag values for each library.
The value for the Pearson correlation coefficient (r)
represents the degree of similarity (the strength of the relationship)
between two libraries and is calculated using the following equation:
 |
where, xi is the number of tags per 100,000 for tag i in the first library and yi is the number of tags per 100,000 for tag i in the second library. For our purposes, n equals 10,000 because 10,000 tags are compared. A dendrogram representing the hierarchical relationships between samples was then generated using hierarchical cluster analysis as described (18) . In addition, the identification of differentially expressed genes was also done using this subset of the SAGE data.
IHC.
Deparafinized 5-µm sections of formalin-fixed ovarian cancer
specimens were submitted to heat-induced antigen retrieval and
processed using the LSAB2 system (DAKO, Carpinteria, CA) with
3,3'-diaminobenzidine as the chromatogen and a hematoxylin
counterstain. Monoclonal antibody against ApoJ/clusterin (clone CLI-9)
was obtained from Alexis Corporation (San Diego, CA) and used at a
1:500 dilution. Monoclonal antibody against Ep-CAM (clone 323/A3)
from NeoMarkers (Fremont, CA) was used at a 1:500 dilution. Polyclonal
antibodies against claudin-3 and -4 were a generous gift from Drs. M.
Furuse and S. Tsukita (Kyoto University, Kyoto, Japan) and were
used at a dilution of 1:1000.
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Results and Discussion |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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. Our libraries included two derived from OSE cells (IOSE29 and
HOSE-4), one derived from immortalized cystadenoma cells (ML-10), three
primary tumors (OVT6, -7, and -8), and four libraries derived
from ovarian cancer cell lines (OV1063, ES-2, A2780, and a pool of cell
lines). Almost 20,000 sequencing reactions were performed, yielding a
total of 384,497 tags of which 82,533 were unique. Accounting for a
SAGE tag error rate of 6.8% (attributable to sequencing errors; see
Ref. 13
), we estimate that we have identified a total of
56,387 genes expressed in ovarian tissues. Except for the A2780 cell
line and the pooled lines (POOL) sample, a minimum of 12,000 genes were
obtained from every library. Typically, for each library, 10% of the
genes were expressed at levels of 
0.01%, and collectively,
these genes accounted for >50% of all of the tags sequenced. Among
the tags that appeared more than once, up to 95% matched to known
sequences in the current GenBank database. For example, of the 6637
tags that appeared more than once in ML10, only 311 had no matches in
the current database, excluding the EST databases. The complete
expression profiles for the 10 libraries described here are
available from the authors or from the CGAP
website.4
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. As expected, the immortalized IOSE29 and ovarian
cystadenoma strain ML10 are much more similar to ovarian tumors than to
colon tumors (average correlation coefficients of 0.70
versus 0.51, respectively; Fig. 1B
). In addition,
IOSE29 and ML10 are very similar to each other, with a correlation
coefficient of 0.82. The primary culture of OSE cells (HOSE-4)
exhibited higher similarities to the ovarian tumors than to the colon
tumors, although the similarity levels were much lower than those
observed for IOSE29. Interestingly, HOSE-4 and IOSE29 appear to be much
more distantly related than expected considering the fact that they
were both derived from "normal" OSE cells. The differences in gene
expression between these cells may be attributable to a number of
factors. The age of the patient, the pathological state of the ovaries,
the presence of nonepithelial cells in the culture, and the fact that
IOSE29 is SV40-immortalized may all contribute to the differences in
gene expression observed. However, it is unlikely that the main
differences are attributable to SV40-immortalization because IOSE29 is
much more similar to normal colon (a non-SV40-immortalized epithelium)
than HOSE-4. It is, of course, possible that the lower degree of
similarity between HOSE-4 and the ovarian tumors compared with IOSE29
and ML-10 reflects the fact that HOSE-4 represents a better
approximation of the normal in vivo OSE cell. A comparative
immunochemical study of carefully chosen gene products in normal
ovarian sections and IOSE29, HOSE-4, and ML-10 cells should allow the
determination of the optimal model for normal OSE.
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. When all of the samples from
Fig. 1B
were included in the hierarchical clustering
analysis, the primary colon tumors clustered with the normal colon
epithelium, but colon cell lines clustered with the ovarian specimens.
Clearly, the tissue clustering that was readily apparent when comparing
primary tissues or immortalized lines was lost when including carcinoma
cell lines. For example, A2780, a widely used ovarian cancer cell line
was just as similar to colon cancer cell lines as it was to ovarian
cancer cell lines. This observation supports the idea that in the
process of establishment, cell lines may lose many of the gene
expression characteristics of their tissue of origin, although
tissue-specific expression is clearly not completely lost in cancer
cell lines (20)
. In addition, the origin of a particular
cell line can be difficult to determine with certainty. The origins of
widely used ovarian cancer cell lines SW626 and OV1063 have recently
been questioned. American Type Culture Collection reported the presence
of a Y chromosome in OV1063, and SW626 displays several characteristics
of colon cancer, including an adenomatous polyposis coli mutation
(21)
. For these reasons, cancer cell lines may not
represent the best model for tissue-specific gene expression studies. It is widely believed that epithelial ovarian cancer and benign ovarian cysts, although not necessarily part of a progression sequence toward malignancy, are both derived from the OSE (22) . OSE cells themselves are mesodermal in origin and are believed to undergo metaplasia before progressing to neoplasia (22 , 23) . On the other hand, it has also been argued that ovarian cancers are not derived from OSE but rather from the secondary Mullerian system, structures lined by Mullerian epithelium but located outside the uterus, cervix, and fallopian tubes (3) . This hypothesis would explain some of the shortcomings of the OSE model, such as the requirement for metaplasia and the lack of well-defined precursors in the ovary. In any event, our results are consistent with the widely accepted dogma of the OSE origin of ovarian cancer. Indeed, IOSE29 showed high degrees of similarity to the ovarian tumors, and both IOSE29 and HOSE were much more closely related to ovarian than colon primary cancers.
E-Cadherin expression has been proposed to be a major determinant in
the formation of metaplastic OSE (14
, 23)
. Consistent with
this hypothesis, E-cadherin was absent in IOSE29, HOSE, and ML10 but
was expressed in all three ovarian tumors (Table 2)
. Other cadherins are also shown for comparison. Interestingly,
VE-cadherin is absent in most libraries except in two of the
preneoplastic ovarian samples, again suggesting metaplasia. As
expected, LI-cadherin was expressed exclusively in the colon-derived
libraries. Interestingly, vimentin, a mesenchymal marker, was present
in essentially all of the ovarian libraries but was very low in the
colon specimens. Although the specificity of vimentin as a mesenchymal
marker has been questioned, this suggests that OSE cells may retain
some of their mesenchymal characteristics, even after the expression of
E-cadherin is turned on.
|
.
Conversely, CK7 was expressed in all three primary ovarian tumors and,
although not absent, was much lower in the colon samples. Examination
of the differential expression patterns of a variety of established
ovarian cancer markers thus provided validation of the SAGE database
and cluster analysis.
Differential Gene Expression.
The ultimate goal of comparing SAGE libraries is to identify
differentially expressed genes. Criteria for differential expression
can be determined for each comparison and transcripts within the
determined range selected for study. We found a large number of genes
that were up-regulated in only one or two of the three tumors on which
SAGE was performed. For example, a total of 444 genes were up-regulated
>10-fold in at least one of the three ovarian primary cancers compared
with IOSE29. However, only 45 genes were overexpressed >10-fold in all
three ovarian tumors analyzed compared with IOSE29. This tumor
heterogeneity is not unexpected but emphasizes the importance of
analyzing multiple specimens for gene expression studies. Our analysis
of three different primary ovarian cancers allowed us to reduce the
number of candidates by looking for consistency between samples.
To identify genes that are very likely to be frequently up-regulated
during ovarian tumorigenesis, we set the following conservative
criteria for our analysis. First, the fold induction was calculated by
adding the number of normalized tags from the three primary tumors and
dividing this number by the total normalized tags in the three
nonmalignant specimens. Cell lines were not included here for reasons
described above. In addition, although HOSE-4 appeared more distantly
related to the other nontransformed specimens, we believe that the
inclusion of HOSE-4, although possibly eliminating real candidates,
makes our analysis more conservative and more likely to identify truly
overexpressed genes in ovarian cancer. Second, all three primary tumors
were required to consistently show elevated levels (>12 tags/100,000)
of the gene in question. This eliminated genes that may be very highly
overexpressed in one tumor but not in others. Finally, the candidate
genes were required to be expressed in at least one ovarian cell line
at a level greater than 3 tags/100,000. This last criterion was used to
reduce the possibility of identifying genes because of their high level
of expression in inflammatory cells or in the stroma of the primary
tumors. Using these criteria, we identified the genes that exhibited
more than 10-fold overexpression (Table 3)
.
|
, 14 were relatively specific for the ovary (HLA-DR, two
different ESTs, GA733-1, ceruloplasmin, glutathione peroxidase-3, the
secretory leukocyte protease inhibitor, ApoJ, ApoE, complement 1r,
BCAM, Mal, HLA-DPB1, and mesothelin), whereas the others were also
expressed in colon tissues. In any event, it is significant that MUC1,
HE4, Ep-CAM, and mesothelin, four genes already known to be
up-regulated in epithelial ovarian cancer (11
, 29, 30, 31)
,
were identified in this study. This fact validates our approach as well
as our set of criteria used to determine the genes differentially
expressed.
Similarly, stringent criteria were used to identify genes
down-regulated in ovarian tumors compared with IOSE29, HOSE-4 and ML10.
Again, the fold difference was calculated by adding tag frequency for
all three "normal" specimens and dividing by the total number of
tags in the three ovarian tumors. A candidate was required to be
expressed at a level of 12 tags/100,000 or greater in all three normal
samples. The genes found elevated >10-fold in normal tissue compared
with tumors are shown in Table 3
. These proteins may be important for
ovarian tissue homeostasis. Indeed, several of these proteins,
(i.e., PAI-1, EMP-3, galectin-1, lysyl oxidase-like 2 and
vinexin-ß) have been implicated in apoptosis, proliferation,
adhesion, or tissue maintenance. Interestingly, a tumor suppressor role
has previously been suggested for lysyl oxidase because it is decreased
in H-ras-transformed cells and up-regulated in
spontaneous revertants of H-ras-transformed fibroblasts
(32)
. Lysyl oxidase-like 2 itself has been implicated in
cellular adhesion and senescence (33)
.
Validation of SAGE Data by IHC of Selected Candidates.
To validate the candidates identified by SAGE, we performed
immunohistochemical analysis of 13 cases of serous cancer of the ovary,
using antibodies against four of the genes identified as up-regulated
in ovarian cancer (Table 3)
. This was particularly important because
the SAGE analysis was initially performed from primary ovarian cancers,
which contain a mixture of cell types. Ep-CAM exhibited diffuse strong
staining of all 13 tumors without blood cell or stromal staining (Fig. 2)
. Importantly, only one of six samples of the OSE present in the cases
showed weak focal staining, and the rest were negative. The strong
immunoreactivity of all 13 ovarian tumors confirms the validity of our
approach to identify genes highly and consistently up-regulated in
ovarian cancer. Similarly, ApoJ was found to be expressed in ovarian
cancer cells and absent from the surface epithelium (Fig. 2)
. Although
some expression was detected in non-tumor stroma and inflammatory
cells, most of the immunoreactivity was in tumor cells, and a majority
(9 of 13) of the cases showed staining. This represents the first
report of ApoJ expression in ovarian cancer and may represent a novel
target for diagnosis or therapy. Claudin-3 and -4 also exhibited
staining limited to the tumor component of the specimens. Most tumor
cells showed strong membrane staining with weak cytoplasmic reactivity
(Fig. 2)
. Some tumor specimens
showed decreased membrane staining with strong cytoplasmic reactivity.
Interestingly, it has been shown that deregulation of the
mitogen-activated protein kinase pathway can lead to
mislocalization of tight junction proteins, including claudin-1
(34)
. The normal surface epithelial component (or
mesothelial cells) examined did not stain or stained only weakly with
the claudin-4 antibody, whereas the determination of claudin-3 levels
in normal epithelium was complicated by a low background reactivity
with this antibody.
|
represent a list of genes relevant
to the tumorigenesis process. Three of the candidates reported here and
validated by IHC, ApoJ, claudin-3 and claudin-4, have never been
previously linked to ovarian cancer. At present, we are evaluating our
numerous candidates, including novel genes, using a variety of
techniques such as quantitative reverse transcription-PCR and IHC. It
will be interesting to identify novel genes that are specifically
expressed in ovarian cancer and tissues. The complete expression
profiles for all of the samples reported here are publicly available
through SAGEmap (17)
and will likely stimulate and
facilitate further research in the field.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Sequencing provided through a contract from the
Cancer Genome Anatomy Project (Contract S98-146A). 
2 To whom requests for reprints should be
addressed, at Laboratory of Biological Chemistry, Gerontology Research
Center, National Institute on Aging, NIH, 5600 Nathan Shock Drive,
Baltimore, MD 21224. Phone: (410) 558-8506; Fax: (410) 558-8386;
E-mail: morinp{at}grc.nia.nih.gov
3 The abbreviations used are: SAGE: serial
analysis of gene expression; OSE, ovarian surface epithelium; FBS,
fetal bovine serum; CGAP, Cancer Genome Anatomy Project; IHC,
immunohistochemistry; ApoJ, apolipoprotein J; Ep-CAM, epithelial
cellular adhesion molecule; CK, cytokeratin; CEA, carcinoembryonic
antigen; ApoE, apolipoprotein E; EST, expressed sequence tag.
4 http://www.ncbi.nlm.nih.gov/SAGE/.
Received 6/ 5/00. Accepted 10/ 3/00.
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REFERENCES |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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A. D. Santin, S. Cane, S. Bellone, M. Palmieri, E. R. Siegel, M. Thomas, J. J. Roman, A. Burnett, M. J. Cannon, and S. Pecorelli Treatment of Chemotherapy-Resistant Human Ovarian Cancer Xenografts in C.B-17/SCID Mice by Intraperitoneal Administration of Clostridium perfringens Enterotoxin Cancer Res., May 15, 2005; 65(10): 4334 - 4342. [Abstract] [Full Text] [PDF]
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R. Drapkin, H. H. von Horsten, Y. Lin, S. C. Mok, C. P. Crum, W. R. Welch, and J. L. Hecht Human Epididymis Protein 4 (HE4) Is a Secreted Glycoprotein that Is Overexpressed by Serous and Endometrioid Ovarian Carcinomas Cancer Res., March 15, 2005; 65(6): 2162 - 2169. [Abstract] [Full Text] [PDF]
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D. T. Rein, M. Breidenbach, T. O. Kirby, T. Han, G. P. Siegal, G. J. Bauerschmitz, M. Wang, D. M. Nettelbeck, Y. Tsuruta, M. Yamamoto, et al. A Fiber-Modified, Secretory Leukoprotease Inhibitor Promoter-Based Conditionally Replicating Adenovirus for Treatment of Ovarian Cancer Clin. Cancer Res., February 1, 2005; 11(3): 1327 - 1335. [Abstract] [Full Text] [PDF]
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Y.-C. Chen, G. Pohl, T.-L. Wang, P. J. Morin, B. Risberg, G. B. Kristensen, A. Yu, B. Davidson, and I.-M. Shih Apolipoprotein E Is Required for Cell Proliferation and Survival in Ovarian Cancer Cancer Res., January 1, 2005; 65(1): 331 - 337. [Abstract] [Full Text] [PDF]
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 K. Hibbs, K. M. Skubitz, S. E. Pambuccian, R. C. Casey, K. M. Burleson, T. R. Oegema Jr, J. J. Thiele, S. M. Grindle, R. L. Bliss, and A. P.N. Skubitz Differential Gene Expression in Ovarian Carcinoma: Identification of Potential Biomarkers Am. J. Pathol., August 1, 2004; 165(2): 397 - 414. [Abstract] [Full Text] [PDF]
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V. A. Heinzelmann-Schwarz, M. Gardiner-Garden, S. M. Henshall, J. Scurry, R. A. Scolyer, M. J. Davies, M. Heinzelmann, L. H. Kalish, A. Bali, J. G. Kench, et al. Overexpression of the Cell Adhesion Molecules DDR1, Claudin 3, and Ep-CAM in Metaplastic Ovarian Epithelium and Ovarian Cancer Clin. Cancer Res., July 1, 2004; 10(13): 4427 - 4436. [Abstract] [Full Text] [PDF]
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I.-M. Shih and R. J. Kurman Ovarian Tumorigenesis: A Proposed Model Based on Morphological and Molecular Genetic Analysis Am. J. Pathol., May 1, 2004; 164(5): 1511 - 1518. [Abstract] [Full Text] [PDF]
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S. L. Kominsky, M. Vali, D. Korz, T. G. Gabig, S. A. Weitzman, P. Argani, and S. Sukumar Clostridium perfringens Enterotoxin Elicits Rapid and Specific Cytolysis of Breast Carcinoma Cells Mediated through Tight Junction Proteins Claudin 3 and 4 Am. J. Pathol., May 1, 2004; 164(5): 1627 - 1633. [Abstract] [Full Text] [PDF]
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A. Moore, Z. Medarova, A. Potthast, and G. Dai In Vivo Targeting of Underglycosylated MUC-1 Tumor Antigen Using a Multimodal Imaging Probe Cancer Res., March 1, 2004; 64(5): 1821 - 1827. [Abstract] [Full Text] [PDF]
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 A. Scorilas, C. A. Borgono, N. Harbeck, J. Dorn, B. Schmalfeldt, M. Schmitt, and E. P. Diamandis Human Kallikrein 13 Protein in Ovarian Cancer Cytosols: A New Favorable Prognostic Marker J. Clin. Oncol., February 15, 2004; 22(4): 678 - 685. [Abstract] [Full Text] [PDF]
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A. Tsuchiya, M. Sakamoto, J. Yasuda, M. Chuma, T. Ohta, M. Ohki, T. Yasugi, Y. Taketani, and S. Hirohashi Expression Profiling in Ovarian Clear Cell Carcinoma: Identification of Hepatocyte Nuclear Factor-1{beta} as a Molecular Marker and a Possible Molecular Target for Therapy of Ovarian Clear Cell Carcinoma Am. J. Pathol., December 1, 2003; 163(6): 2503 - 2512. [Abstract] [Full Text]
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K. K. Zorn, A. A. Jazaeri, C. S. Awtrey, G. J. Gardner, S. C. Mok, J. Boyd, and M. J. Birrer Choice of Normal Ovarian Control Influences Determination of Differentially Expressed Genes in Ovarian Cancer Expression Profiling Studies Clin. Cancer Res., October 15, 2003; 9(13): 4811 - 4818. [Abstract] [Full Text] [PDF]
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P. Michl, C. Barth, M. Buchholz, M. M. Lerch, M. Rolke, K.-H. Holzmann, A. Menke, H. Fensterer, K. Giehl, M. Lohr, et al. Claudin-4 Expression Decreases Invasiveness and Metastatic Potential of Pancreatic Cancer Cancer Res., October 1, 2003; 63(19): 6265 - 6271. [Abstract] [Full Text] [PDF]
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G. J. Gordon, W. G. Richards, D. J. Sugarbaker, M. T. Jaklitsch, and R. Bueno A Prognostic Test for Adenocarcinoma of the Lung From Gene Expression Profiling Data Cancer Epidemiol. Biomarkers Prev., September 1, 2003; 12(9): 905 - 910. [Abstract] [Full Text] [PDF]
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P. Buckhaults, Z. Zhang, Y.-C. Chen, T.-L. Wang, B. St. Croix, S. Saha, A. Bardelli, P. J. Morin, K. Polyak, R. H. Hruban, et al. Identifying Tumor Origin Using a Gene Expression-based Classification Map Cancer Res., July 15, 2003; 63(14): 4144 - 4149. [Abstract] [Full Text] [PDF]
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I. Hellstrom, J. Raycraft, M. Hayden-Ledbetter, J. A. Ledbetter, M. Schummer, M. McIntosh, C. Drescher, N. Urban, and K. E. Hellstrom The HE4 (WFDC2) Protein Is a Biomarker for Ovarian Carcinoma Cancer Res., July 1, 2003; 63(13): 3695 - 3700. [Abstract] [Full Text] [PDF]
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L. B. A. Rangel, R. Agarwal, T. D'Souza, E. S. Pizer, P. L. Alo, W. D. Lancaster, L. Gregoire, D. R. Schwartz, K. R. Cho, and P. J. Morin Tight Junction Proteins Claudin-3 and Claudin-4 Are Frequently Overexpressed in Ovarian Cancer but Not in Ovarian Cystadenomas Clin. Cancer Res., July 1, 2003; 9(7): 2567 - 2575. [Abstract] [Full Text] [PDF]
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R. C. Bast Jr Status of Tumor Markers in Ovarian Cancer Screening J. Clin. Oncol., May 15, 2003; 21(90100): 200s - 205. [Abstract] [Full Text] [PDF]
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N. Devoogdt, G. Hassanzadeh Ghassabeh, J. Zhang, L. Brys, P. De Baetselier, and H. Revets Secretory leukocyte protease inhibitor promotes the tumorigenic and metastatic potential of cancer cells PNAS, May 13, 2003; 100(10): 5778 - 5782. [Abstract] [Full Text] [PDF]
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 A. Riesewijk, J. Martin, R. van Os, J. A. Horcajadas, J. Polman, A. Pellicer, S. Mosselman, and C. Simon Gene expression profiling of human endometrial receptivity on days LH+2 versus LH+7 by microarray technology Mol. Hum. Reprod., May 1, 2003; 9(5): 253 - 264. [Abstract] [Full Text] [PDF]
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Y. Chen, C. Miller, R. Mosher, X. Zhao, J. Deeds, M. Morrissey, B. Bryant, D. Yang, R. Meyer, F. Cronin, et al. Identification of Cervical Cancer Markers by cDNA and Tissue Microarrays Cancer Res., April 15, 2003; 63(8): 1927 - 1935. [Abstract] [Full Text] [PDF]
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 G. Singer, R. Oldt III, Y. Cohen, B. G. Wang, D. Sidransky, R. J. Kurman, and I.-M. Shih Mutations in BRAF and KRAS Characterize the Development of Low-Grade Ovarian Serous Carcinoma J Natl Cancer Inst, March 19, 2003; 95(6): 484 - 486. [Abstract] [Full Text] [PDF]
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W.'e. El-Rifai, C. A. Moskaluk, M. K. Abdrabbo, J. Harper, C. Yoshida, G. J. Riggins, H. F. Frierson Jr., and S. M. Powell Gastric Cancers Overexpress S100A Calcium-binding Proteins Cancer Res., December 1, 2002; 62(23): 6823 - 6826. [Abstract] [Full Text] [PDF]
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J. L. Dennis, J. K. Vass, E. C. Wit, W. N. Keith, and K. A. Oien Identification from Public Data of Molecular Markers of Adenocarcinoma Characteristic of the Site of Origin Cancer Res., November 1, 2002; 62(21): 5999 - 6005. [Abstract] [Full Text] [PDF]
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K. Boon, E. C. Osorio, S. F. Greenhut, C. F. Schaefer, J. Shoemaker, K. Polyak, P. J. Morin, K. H. Buetow, R. L. Strausberg, S. J. de Souza, et al. An anatomy of normal and malignant gene expression PNAS, August 20, 2002; 99(17): 11287 - 11292. [Abstract] [Full Text] [PDF]
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P. Seth, D. Porter, J. Lahti-Domenici, Y. Geng, A. Richardson, and K. Polyak Cellular and Molecular Targets of Estrogen in Normal Human Breast Tissue Cancer Res., August 15, 2002; 62(16): 4540 - 4544. [Abstract] [Full Text] [PDF]
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D. R. Schwartz, S. L. R. Kardia, K. A. Shedden, R. Kuick, G. Michailidis, J. M. G. Taylor, D. E. Misek, R. Wu, Y. Zhai, D. M. Darrah, et al. Gene Expression in Ovarian Cancer Reflects Both Morphology and Biological Behavior, Distinguishing Clear Cell from Other Poor-Prognosis Ovarian Carcinomas Cancer Res., August 15, 2002; 62(16): 4722 - 4729. [Abstract] [Full Text] [PDF]
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A. A. Jazaeri, C. J. Yee, C. Sotiriou, K. R. Brantley, J. Boyd, and E. T. Liu Gene Expression Profiles of BRCA1-Linked, BRCA2-Linked, and Sporadic Ovarian Cancers J Natl Cancer Inst, July 3, 2002; 94(13): 990 - 1000. [Abstract] [Full Text] [PDF]
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J. Bayani, J. D. Brenton, P. F. Macgregor, B. Beheshti, M. Albert, D. Nallainathan, J. Karaskova, B. Rosen, J. Murphy, S. Laframboise, et al. Parallel Analysis of Sporadic Primary Ovarian Carcinomas by Spectral Karyotyping, Comparative Genomic Hybridization, and Expression Microarrays Cancer Res., June 1, 2002; 62(12): 3466 - 3476. [Abstract] [Full Text] [PDF]
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G. P. Sawiris, C. A. Sherman-Baust, K. G. Becker, C. Cheadle, D. Teichberg, and P. J. Morin Development of a Highly Specialized cDNA Array for the Study and Diagnosis of Epithelial Ovarian Cancer Cancer Res., May 1, 2002; 62(10): 2923 - 2928. [Abstract] [Full Text] [PDF]
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C. A. Iacobuzio-Donahue, A. Maitra, G. L. Shen-Ong, T. van Heek, R. Ashfaq, R. Meyer, K. Walter, K. Berg, M. A. Hollingsworth, J. L. Cameron, et al. Discovery of Novel Tumor Markers of Pancreatic Cancer using Global Gene Expression Technology Am. J. Pathol., April 1, 2002; 160(4): 1239 - 1249. [Abstract] [Full Text] [PDF]
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A. Wallqvist, A. A. Rabow, R. H. Shoemaker, E. A. Sausville, and D. G. Covell Establishing Connections between Microarray Expression Data and Chemotherapeutic Cancer Pharmacology Mol. Cancer Ther., March 1, 2002; 1(5): 311 - 320. [Abstract] [Full Text] [PDF]
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B. Ryu, J. Jones, N. J. Blades, G. Parmigiani, M. A. Hollingsworth, R. H. Hruban, and S. E. Kern Relationships and Differentially Expressed Genes among Pancreatic Cancers Examined by Large-scale Serial Analysis of Gene Expression Cancer Res., February 1, 2002; 62(3): 819 - 826. [Abstract] [Full Text] [PDF]
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Y. Hippo, H. Taniguchi, S. Tsutsumi, N. Machida, J.-M. Chong, M. Fukayama, T. Kodama, and H. Aburatani Global Gene Expression Analysis of Gastric Cancer by Oligonucleotide Microarrays Cancer Res., January 1, 2002; 62(1): 233 - 240. [Abstract] [Full Text] [PDF]
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C. A. Iacobuzio-Donahue, B. Ryu, R. H. Hruban, and S. E. Kern Exploring the Host Desmoplastic Response to Pancreatic Carcinoma : Gene Expression of Stromal and Neoplastic Cells at the Site of Primary Invasion Am. J. Pathol., January 1, 2002; 160(1): 91 - 99. [Abstract] [Full Text] [PDF]
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H. Long, C. D. Crean, W.-H. Lee, O. W. Cummings, and T. G. Gabig Expression of Clostridium Perfringens Enterotoxin Receptors Claudin-3 and Claudin-4 in Prostate Cancer Epithelium Cancer Res., November 1, 2001; 61(21): 7878 - 7881. [Abstract] [Full Text] [PDF]
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G. B. Mills, R. C. Bast Jr., and S. Srivastava Future for Ovarian Cancer Screening: Novel Markers From Emerging Technologies of Transcriptional Profiling and Proteomics J Natl Cancer Inst, October 3, 2001; 93(19): 1437 - 1439. [Full Text] [PDF]
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 S. A. Greenberg DNA microarray gene expression analysis technology and its application to neurological disorders Neurology, September 11, 2001; 57(5): 755 - 761. [Abstract] [Full Text] [PDF]
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V. Shridhar, J. Lee, A. Pandita, S. Iturria, R. Avula, J. Staub, M. Morrissey, E. Calhoun, A. Sen, K. Kalli, et al. Genetic Analysis of Early- versus Late-Stage Ovarian Tumors Cancer Res., August 1, 2001; 61(15): 5895 - 5904. [Abstract] [Full Text] [PDF]
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K. Polyak and G. J. Riggins Gene Discovery Using the Serial Analysis of Gene Expression Technique: Implications for Cancer Research J. Clin. Oncol., June 1, 2001; 19(11): 2948 - 2958. [Abstract] [Full Text] [PDF]
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C. D. Hough, K. R. Cho, A. B. Zonderman, D. R. Schwartz, and P. J. Morin Coordinately Up-Regulated Genes in Ovarian Cancer Cancer Res., May 1, 2001; 61(10): 3869 - 3876. [Abstract] [Full Text]
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 G. J. Riggins and R. L. Strausberg Genome and genetic resources from the Cancer Genome Anatomy Project Hum. Mol. Genet., April 1, 2001; 10(7): 663 - 667. [Abstract] [Full Text] [PDF]
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H. Naora, Y. Yang, F. J. Montz, J. D. Seidman, R. J. Kurman, and R. B. S. Roden A serologically identified tumor antigen encoded by a homeobox gene promotes growth of ovarian epithelial cells PNAS, March 27, 2001; 98(7): 4060 - 4065. [Abstract] [Full Text] [PDF]
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H. Naora, F. J. Montz, C.-Y. Chai, and R. B. S. Roden Aberrant expression of homeobox gene HOXA7 is associated with mullerian-like differentiation of epithelial ovarian tumors and the generation of a specific autologous antibody response PNAS, December 18, 2001; 98(26): 15209 - 15214. [Abstract] [Full Text] [PDF]
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