Qiqin Yin-Goen

Angiogenic and lymphangiogenic microvessel density in breast carcinoma: correlation with clinicopathologic parameters and VEGF-family gene expression

Angiogenesis and lymphangiogenesis are essential for breast cancer progression and are regulated by vascular endothelial growth factors (VEGF). To determine clinical and molecular correlates of these processes, we measured blood and lymphatic vascular microvessel density in 29 invasive carcinomas (22 ductal, six lobular, one papillary), using the vascular marker CD31 and the novel lymphatic marker D2-40. Microvessel density was assessed microscopically and by image cytometry, and was compared with tumor histology, grade, stage, lymph node metastasis, hormone receptors, HER2/neu status, and expression of VEGF, VEGF-C and VEGF-D by immunohistochemistry or quantitative RT-PCR. Strong correlation was observed between visual and image cytometric microvessel density using D2-40 but not CD31 (P=0.016 and 0.1521, respectively). Image cytometric CD31 microvessel density correlated with tumor size, grade, stage and lymph node metastasis (P=0.0001, 0.0107, 0.0035 and 0.0395, respectively). D2-40 microvessel density correlated with tumor stage (P=0.0123 by image cytometry) and lymph node metastasis (P=0.0558 by microscopy). Immunohistochemical VEGF signal in peritumoral blood vessels correlated with image cytometric CD31 and D2-40 microvessel density (P=0.022 and 0.0012, respectively), consistent with the role of VEGF in blood and lymphatic vascular growth. Intratumoral VEGF-C and VEGF-D expression by quantitative RT-PCR correlated with D2-40 (P=0.0291 by image cytometry) but not with CD31 microvessel density, which could suggest a selective role of VEGF-C and VEGF-D in lymphangiogenesis. CD31 and D2-40 microvessel density correlated significantly with several prognostic factors, including lymph node metastasis. Thus, measurements of angiogenesis and lymphangiogenesis may have utility for breast cancer pathology, particularly for estimation of metastatic risk.

Molecular classification of renal tumors by gene expression profiling.

Renal tumor classification is important because histopathological subtypes are associated with distinct clinical behavior. However, diagnosis is difficult because tumor subtypes have overlapping microscopic characteristics. Therefore, ancillary methods are needed to optimize classification. We used oligonucleotide microarrays to analyze 31 adult renal tumors, including clear cell renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, oncocytoma, and angiomyolipoma. Expression profiles correlated with histopathology; unsupervised algorithms clustered 30 of 31 tumors according to appropriate diagnostic subtypes while supervised analyses identified significant, subtype-specific expression markers. Clear cell RCC overexpressed proximal nephron, angiogenic, and immune response genes, chromophobe RCC oncocytoma overexpressed distal nephron and oxidative phosphorylation genes, papillary RCC overexpressed serine protease inhibitors, and extracellular matrix products, and angiomyolipoma overexpressed muscle developmental, lipid biosynthetic, melanocytic, and distinct angiogenic factors. Quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry of formalin-fixed renal tumors confirmed overexpression of proximal nephron markers (megalin/low-density lipoprotein-related protein 2, alpha-methylacyl CoA racemase) in clear cell and papillary RCC and distal nephron markers (beta-defensin 1, claudin 7) in chromophobe RCC/oncocytoma. In summary, renal tumor subtypes were classified by distinct gene expression profiles, illustrating tumor pathobiology and translating into novel molecular bioassays using fixed tissue.

Loss of HOXC6 expression induces apoptosis in prostate cancer cells

We have performed whole genome expression profiling of 28 patient prostate tumor samples and 12 normal prostate samples and identified 55 upregulated and 60 downregulated genes significantly changed in prostate tumor samples compared to normal prostate tissues. Among the members of the upregulated gene set was the developmental transcription factor Homeobox C6 (HOXC6). Silencing of HOXC6 expression using small-interfering RNA (siRNA) resulted in decreased proliferation rates for both androgen-dependent LnCaP cells and the LnCaP-derived androgen-independent C4-2 cell line. Flow cytometry and immunoblotting for the caspase-cleaved form of poly-ADP ribose polymerase (PARP) determined that the decrease in cell numbers was due to increased apoptosis. To validate the specificity of the siRNA-induced apoptosis, LnCaP cells were cotransfected with siRNA specific to the HOXC6 3′UTR and a mammalian expression vector containing the HOXC6 open reading frame, but lacking the 3′UTR. Overexpression of HOXC6 rescued the LnCaP cells from HOXC6 siRNA-induced apoptosis, and increased growth of control GFP siRNA-transfected cells. Expression profiling of HOXC6 siRNA transfections and HOXC6 overexpression identified neutral endopeptidase (NEP) and insulin-like growth factor binding protein-3 (IGFBP-3) as potential proapoptotic repression targets of HOXC6. Our data suggest that HOXC6 may be a novel potential therapeutic target for prostate cancer.

Claudin-7 Immunohistochemistry in Renal Tumors: A Candidate Marker for Chromophobe Renal Cell Carcinoma Identified by Gene Expression Profiling

CONTEXT The differential diagnosis of eosinophilic renal tumors can be difficult by light microscopy. In particular, chromophobe renal cell carcinoma (RCC) is difficult to distinguish from oncocytoma. This differential diagnosis is important because chromophobe RCC is malignant, whereas oncocytoma is benign. Furthermore, chromophobe RCC has distinct malignant potential and prognosis compared with eosinophilic variants of other RCC subtypes. Immunohistochemistry is useful for distinguishing chromophobe RCC from other subtypes of renal carcinoma, but no expression marker reliably separates chromophobe RCC from oncocytoma. OBJECTIVE In a previous gene expression microarray analysis of renal tumor subtypes, we found the distal nephron markers claudin-7 and claudin-8 to be overexpressed in chromophobe RCC versus oncocytoma and other tumor subtypes. We have confirmed similar findings in independent microarray data and validated differential claudin-7 protein expression by immunohistochemistry. DESIGN Immunohistochemical analysis of claudin-7 in 36 chromophobe RCCs, 43 oncocytomas, 42 clear cell RCCs, and 29 papillary RCCs. RESULTS Membranous claudin-7 expression was detected in 67% chromophobe RCCs, compared with 0% clear cell RCCs, 28% papillary RCCs, and 26% oncocytomas (P < .001). CONCLUSIONS Based on microarray and immunohistochemical data, we propose claudin-7 to be a candidate expression marker for distinguishing chromophobe RCC from other renal tumor subtypes, including the morphologically similar oncocytoma. The clinical utility of claudin-7 should be validated in independent studies of renal tumors, possibly in combination with additional targets in a multiplex immunohistochemical panel.

Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis

The differential diagnosis of clear cell, papillary, and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative reverse transcriptase-polymerase chain reaction can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary, and 10 chromophobe renal cell carcinomas; the microarray data was derived from 2 independent published studies. The ArrayWiki biocomputing system merged the microarray data sets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected 6 novel candidate biomarkers from the omniBioMarker analysis and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a, and acetyl-CoA acetyltransferase 1. Quantitative reverse transcriptase-polymerase chain reaction was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary, and 12 chromophobe renal cell carcinomas. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.

Gene expression profiling of clear cell papillary renal cell carcinoma: comparison with clear cell renal cell carcinoma and papillary renal cell carcinoma

Clear cell papillary renal cell carcinoma is a distinct variant of renal cell carcinoma that shares some overlapping histological and immunohistochemical features of clear cell renal cell carcinoma and papillary renal cell carcinoma. Although the clear cell papillary renal cell carcinoma immunohistochemical profile is well described, clear cell papillary renal cell carcinoma mRNA expression has not been well characterized. We investigated the clear cell papillary renal cell carcinoma gene expression profile using previously identified candidate genes. We selected 17 clear cell papillary renal cell carcinoma, 15 clear cell renal cell carcinoma, and 13 papillary renal cell carcinoma cases for molecular analysis following histological review. cDNA from formalin-fixed paraffin-embedded tissue was prepared. Quantitative real-time PCR targeting alpha-methylacyl coenzyme-A racemase (AMACR), BMP and activin membrane-bound inhibitor homolog (BAMBI), carbonic anhydrase IX (CA9), ceruloplasmin (CP), nicotinamide N-methyltransferase (NNMT), schwannomin-interacting protein 1 (SCHIP1), solute carrier family 34 (sodium phosphate) member 2 (SLC34A2), and vimentin (VIM) was performed. Gene expression data were normalized relative to 28S ribosomal RNA. Clear cell papillary renal cell carcinoma expressed all eight genes at variable levels. Compared with papillary renal cell carcinoma, clear cell papillary renal cell carcinoma expressed more CA9, CP, NNMT, and VIM, less AMACR, BAMBI, and SLC34A2, and similar levels of SCHIP1. Compared with clear cell renal cell carcinoma, clear cell papillary renal cell carcinoma expressed slightly less NNMT, but similar levels of the other seven genes. Although clear cell papillary renal cell carcinoma exhibits a unique molecular signature, it expresses several genes at comparable levels to clear cell renal cell carcinoma relative to papillary renal cell carcinoma. Understanding the molecular pathogenesis of clear cell papillary renal cell carcinoma will have a key role in future sub-classifications of this unique tumor.

caCORRECT2: Improving the accuracy and reliability of microarray data in the presence of artifacts

BackgroundIn previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECTs web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu.ResultsWe demonstrate that (1) caCORRECTs artifact-aware normalization avoids the undesirable global data warping that happens when any damaged chips are processed without caCORRECT; (2) When used upstream of RMA, PLIER, or MAS5.0, the data imputation of caCORRECT generally improves the accuracy of microarray gene expression in the presence of artifacts more than using Harshlighting or not using any quality control; (3) Biomarkers selected from artifactual microarray data which have undergone the quality control procedures of caCORRECT are more likely to be reliable, as shown by both spike in and PCR validation experiments. Finally, we present a case study of the use of caCORRECT to reliably identify biomarkers for renal cell carcinoma, yielding two diagnostic biomarkers with potential clinical utility, PRKAB1 and NNMT.ConclusionscaCORRECT is shown to improve the accuracy of gene expression, and the reproducibility of experimental results in clinical application. This study suggests that caCORRECT will be useful to clean up possible artifacts in new as well as archived microarray data.

Improving the efficiency of biomarker identification using biological knowledge.

Identifying and validating biomarkers from high-throughput gene expression data is important for understanding and treating cancer. Typically, we identify candidate biomarkers as features that are differentially expressed between two or more classes of samples. Many feature selection metrics rely on ranking by some measure of differential expression. However, interpreting these results is difficult due to the large variety of existing algorithms and metrics, each of which may produce different results. Consequently, a feature ranking metric may work well on some datasets but perform considerably worse on others. We propose a method to choose an optimal feature ranking metric on an individual dataset basis. A metric is optimal if, for a particular dataset, it favorably ranks features that are known to be relevant biomarkers. Extensive knowledge of biomarker candidates is available in public databases and literature. Using this knowledge, we can choose a ranking metric that produces the most biologically meaningful results. In this paper, we first describe a framework for assessing the ability of a ranking metric to detect known relevant biomarkers. We then apply this method to clinical renal cancer microarray data to choose an optimal metric and identify several candidate biomarkers.

Expression of C-reactive protein and cyclooxygenase enzyme-2 in clear cell renal cell carcinoma: correlation with pathological parameters in 110 patients

C-reactive protein is produced in response to cytokines such as interleukin (IL)-6. It is known that increased plasma IL-6 levels induce increased hepatic and intratumoral production of C-reactive protein. Cyclooxygenase enzyme-2 is induced by various stimuli, including inflammation and various growth factors. Expression of these two markers has not been well studied in clear cell renal cell carcinoma. The objective of this study is to correlate the expression of C-reactive protein and cyclooxygenase enzyme-2 in clear cell renal cell carcinoma with pathologic parameters. A search of the surgical pathology and consultation files at our institution was performed for nephrectomy specimens with clear cell renal cell carcinoma from 2007 to 2008. Immunohistochemical stains for C-reactive protein and cyclooxygenase enzyme-2 were performed. Staining intensity was graded as 0, 1+, 2+, and 3+. The staining intensity was then correlated with pathologic stage and Fuhrman nuclear grade for each case. A total of 110 cases were identified. Strong expression of C-reactive protein was associated with higher Fuhrman nuclear grade and pathologic stage, and the strength of correlation was statistically significant (p = 0.01 and p = 0.001), respectively. However, cyclooxygenase enzyme-2 expression did not show statistically significant correlation with both pathologic stage and Fuhrman nuclear grade (p = 0.1 and p = 0.15), respectively. To our knowledge, this is the largest study to date correlating the expression of both C-reactive protein and cyclooxygenase enzyme-2 in tissue with pathologic parameters in patients with clear cell renal cell carcinoma, which could have significant prognostic and therapeutic implications.

Emerging translational bioinformatics: Knowledge-guided biomarker identification for cancer diagnostics

Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. In this paper, we describe emerging technology for enabling translational bioinformatics by improving biomarker identification. Specifically, we present an application that uses prior knowledge to identify the most biologically relevant gene ranking algorithm. Identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data — e.g., high technical variability, small sample size, and high dimension size. Furthermore, due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. As a case study, we apply these knowledge-driven methods to renal cancer data and identify genes that are potential biomarkers for cancer subtype classification.