DNA Damage Response is Prominent in Ovarian High-Grade Serous Carcinomas, Especially Those with Rsf-1 (HBXAP) Overexpression

DNA damage commonly occurs in cancer cells as a result of endogenous and tumor microenvironmental stress. In this study, we applied immunohistochemistry to study the expression of phosphorylated Chk2 (pChk2), a surrogate marker of the DNA damage response, in high grade and low grade of ovarian serous carcinoma. A phospho-specific antibody specific for threonine 68 of Chk2 was used for immunohistochemistry on a total of 292 ovarian carcinoma tissues including 250 high-grade and 42 low-grade serous carcinomas. Immunostaining intensity was correlated with clinicopathological features. We found that there was a significant correlation between pChk2 immunostaining intensity and percentage of pChk2 positive cells in tumors and demonstrated that high-grade serous carcinomas expressed an elevated level of pChk2 as compared to low-grade serous carcinomas. Normal ovarian, fallopian tube, ovarian cyst, and serous borderline tumors did not show detectable pChk2 immunoreactivity. There was no significant difference in pChk2 immunoreactivity between primary and recurrent high-grade serous carcinomas. In high-grade serous carcinomas, a significant correlation (P < 0.0001) in expression level (both in intensity and percentage) was found between pChk2 and Rsf-1 (HBXAP), a gene involved in chromatin remodeling that is amplified in high-grade serous carcinoma. Our results suggest that the DNA damage response is common in high-grade ovarian serous carcinomas, especially those with Rsf-1 overexpression, suggesting that Rsf-1 may be associated with DNA damage response in high-grade serous carcinomas

Homogeneous point mutation detection by quantum dot-mediated two-color fluorescence coincidence analysis

This report describes a new genotyping method capable of detecting low-abundant point mutations in a homogeneous, separation-free format. The method is based on integration of oligonucleotide ligation with a semiconductor quantum dot (QD)-mediated two-color fluorescence coincidence detection scheme. Surface-functionalized QDs are used to capture fluorophore-labeled ligation products, forming QD-oligonucleotide nanoassemblies. The presence of such nanoassemblies and thereby the genotype of the sample is determined by detecting the simultaneous emissions of QDs and fluorophores that occurs whenever a single nanoassembly flows through the femtoliter measurement volume of a confocal fluorescence detection system. The ability of this method to detect single events enables analysis of target signals with a multiple-parameter (intensities and count rates of the digitized target signals) approach to enhance assay sensitivity and specificity. We demonstrate that this new method is capable of detecting zeptomoles of targets and achieve an allele discrimination selectivity factor >10(5)

Precursor Lesions of High-Grade Serous Ovarian Carcinoma: Morphological and Molecular Characteristics

The lack of proven screening tools for early detection and the high mortality of ovarian serous carcinoma (OSC), particularly high grade, have focused attention on identifying putative precursor lesions with distinct morphological and molecular characteristics. The finding of occult invasive and intraepithelial fallopian tube carcinomas in prophylactically removed specimens from asymptomatic high-risk BRCA 1/2-mutation carriers supports the notion of an origin for OSC in the fallopian tube. The intraepithelial carcinomas have been referred to as serous intraepithelial carcinomas (STICs) but our own findings (unpublished data) and recent reports have drawn attention to a spectrum of changes that fall short of STICs that we have designated serous tubal intraepithelial lesions (STILs)

Extrauterine inflammatory myofibroblastic tumor: A case report

• This is the first case report of inflammatory myofibroblastic tumor in the literature to present with extrauterine disease. • A prompt work-up of symptoms may have precluded a tumor debulking procedure

Knowledge-guided multi-scale independent component analysis for biomarker identification

<p>Abstract</p> <p>Background</p> <p>Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.</p> <p>Results</p> <p>Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.</p> <p>Conclusion</p> <p>We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.</p

Lack of a Y-Chromosomal Complement in the Majority of Gestational Trophoblastic Neoplasms

Gestational trophoblastic neoplasms (GTNs) are a rare group of neoplastic diseases composed of choriocarcinomas, placental site trophoblastic tumors (PSTTs) and epithelioid trophoblastic tumors (ETTs). Since these tumors are derivatives of fetal trophoblastic tissue, approximately 50% of GTN cases are expected to originate from a male conceptus and carry a Y-chromosomal complement according to a balanced sex ratio. To investigate this hypothesis, we carried out a comprehensive analysis by genotyping a relatively large sample size of 51 GTN cases using three independent sex chromosome genetic markers; Amelogenin, Protein Kinase and Zinc Finger have X and Y homologues that are distinguishable by their PCR product size. We found that all cases contained the X-chromosomal complement while only five (10%) of 51 tumors harbored the Y-chromosomal complement. Specifically, Y-chromosomal signals were detected in one (5%) of 19 choriocarcinomas, one (7%) of 15 PSTTs and three (18%) of 17 ETTs. The histopathological features of those with a Y-chromosome were similar to those without. Our results demonstrate the presence of a Y-chromosomal complement in GTNs, albeit a low 10% of cases. This shortfall of Y-chromosomal complements in GTNs may reinforce the notion that the majority of GTNs are derived from previous molar gestations

Precursor lesions of high-grade serous ovarian carcinoma: morphological and molecular characteristics

The lack of proven screening tools for early detection and the high mortality of ovarian serous carcinoma (OSC), particularly high grade, have focused attention on identifying putative precursor lesions with distinct morphological and molecular characteristics. The finding of occult invasive and intraepithelial fallopian tube carcinomas in prophylactically removed specimens from asymptomatic high-risk BRCA 1/2-mutation carriers supports the notion of an origin for OSC in the fallopian tube. The intraepithelial carcinomas have been referred to as serous intraepithelial carcinomas (STICs) but our own findings (unpublished data) and recent reports have drawn attention to a spectrum of changes that fall short of STICs that we have designated serous tubal intraepithelial lesions (STILs)

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.Comment: 7 pages, 7 figure